{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5hIbr52I7Z7U"
   },
   "source": [
    "Deep Learning\n",
    "=============\n",
    "\n",
    "Assignment 1\n",
    "------------\n",
    "\n",
    "The objective of this assignment is to learn about simple data curation practices, and familiarize you with some of the data we'll be reusing later.\n",
    "\n",
    "This notebook uses the [notMNIST](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html) dataset to be used with python experiments. This dataset is designed to look like the classic [MNIST](http://yann.lecun.com/exdb/mnist/) dataset, while looking a little more like real data: it's a harder task, and the data is a lot less 'clean' than MNIST."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "apJbCsBHl-2A"
   },
   "outputs": [],
   "source": [
    "# These are all the modules we'll be using later. Make sure you can import them\n",
    "# before proceeding further.\n",
    "from __future__ import print_function\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import sys\n",
    "import tarfile\n",
    "from IPython.display import display, Image\n",
    "from scipy import ndimage\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from six.moves.urllib.request import urlretrieve\n",
    "from six.moves import cPickle as pickle\n",
    "\n",
    "# Need to use matplotlib inline to produce plots inside jupyter notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jNWGtZaXn-5j"
   },
   "source": [
    "First, we'll download the dataset to our local machine. The data consists of characters rendered in a variety of fonts on a 28x28 image. The labels are limited to 'A' through 'J' (10 classes). The training set has about 500k and the testset 19000 labelled examples. Given these sizes, it should be possible to train models quickly on any machine."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Middle'>The following cell will not run without internet connection. Be sure to acquire the files in another way</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 186058,
     "status": "ok",
     "timestamp": 1444485672507,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2a0a5e044bb03b66",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "EYRJ4ICW6-da",
    "outputId": "0d0f85df-155f-4a89-8e7e-ee32df36ec8d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found and verified notMNIST_large.tar.gz\n",
      "Found and verified notMNIST_small.tar.gz\n"
     ]
    }
   ],
   "source": [
    "url = 'http://commondatastorage.googleapis.com/books1000/'\n",
    "last_percent_reported = None\n",
    "\n",
    "def download_progress_hook(count, blockSize, totalSize):\n",
    "    \"\"\"A hook to report the progress of a download. This is mostly intended for users with\n",
    "    slow internet connections. Reports every 1% change in download progress.\n",
    "    \"\"\"\n",
    "    global last_percent_reported\n",
    "    percent = int(count * blockSize * 100 / totalSize)\n",
    "\n",
    "    if last_percent_reported != percent:\n",
    "        if percent % 5 == 0:\n",
    "            sys.stdout.write(\"%s%%\" % percent)\n",
    "            sys.stdout.flush()\n",
    "        else:\n",
    "            sys.stdout.write(\".\")\n",
    "            sys.stdout.flush()\n",
    "        last_percent_reported = percent\n",
    "        \n",
    "def maybe_download(filename, expected_bytes, force=False):\n",
    "    \"\"\"Download a file if not present, and make sure it's the right size.\"\"\"\n",
    "    if force or not os.path.exists(filename):\n",
    "        print('Attempting to download:', filename) \n",
    "        filename, _ = urlretrieve(url + filename, filename, reporthook=download_progress_hook)\n",
    "        print('\\nDownload Complete!')\n",
    "    statinfo = os.stat(filename)\n",
    "    if statinfo.st_size == expected_bytes:\n",
    "        print('Found and verified', filename)\n",
    "    else:\n",
    "        raise Exception('Failed to verify ' + filename + '. Can you get to it with a browser?')\n",
    "    return filename\n",
    "\n",
    "train_filename = maybe_download('notMNIST_large.tar.gz', 247336696)\n",
    "test_filename = maybe_download('notMNIST_small.tar.gz', 8458043)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cC3p0oEyF8QT"
   },
   "source": [
    "Extract the dataset from the compressed .tar.gz file.\n",
    "This should give you a set of directories, labelled A through J."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 186055,
     "status": "ok",
     "timestamp": 1444485672525,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2a0a5e044bb03b66",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "H8CBE-WZ8nmj",
    "outputId": "ef6c790c-2513-4b09-962e-27c79390c762"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large already present - Skipping extraction of notMNIST_large.tar.gz.\n",
      "['notMNIST_large/A', 'notMNIST_large/B', 'notMNIST_large/C', 'notMNIST_large/D', 'notMNIST_large/E', 'notMNIST_large/F', 'notMNIST_large/G', 'notMNIST_large/H', 'notMNIST_large/I', 'notMNIST_large/J']\n",
      "notMNIST_small already present - Skipping extraction of notMNIST_small.tar.gz.\n",
      "['notMNIST_small/A', 'notMNIST_small/B', 'notMNIST_small/C', 'notMNIST_small/D', 'notMNIST_small/E', 'notMNIST_small/F', 'notMNIST_small/G', 'notMNIST_small/H', 'notMNIST_small/I', 'notMNIST_small/J']\n"
     ]
    }
   ],
   "source": [
    "num_classes = 10\n",
    "np.random.seed(133)\n",
    "\n",
    "def maybe_extract(filename, force=False):\n",
    "    root = os.path.splitext(os.path.splitext(filename)[0])[0]  # remove .tar.gz\n",
    "    if os.path.isdir(root) and not force:\n",
    "    # You may override by setting force=True.\n",
    "        print('%s already present - Skipping extraction of %s.' % (root, filename))\n",
    "    else:\n",
    "        print('Extracting data for %s. This may take a while. Please wait.' % root)\n",
    "        tar = tarfile.open(filename)\n",
    "        sys.stdout.flush()\n",
    "        tar.extractall()\n",
    "        tar.close()\n",
    "    data_folders = [os.path.join(root, d) for d in sorted(os.listdir(root))\n",
    "        if os.path.isdir(os.path.join(root, d))]\n",
    "    if len(data_folders) != num_classes:\n",
    "        raise Exception(\n",
    "            'Expected %d folders, one per class. Found %d instead.' % (\n",
    "            num_classes, len(data_folders)))\n",
    "    print(data_folders)\n",
    "    return data_folders\n",
    "  \n",
    "train_folders = maybe_extract(train_filename)\n",
    "test_folders = maybe_extract(test_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4riXK3IoHgx6"
   },
   "source": [
    "---\n",
    "Problem 1\n",
    "---------\n",
    "\n",
    "Let's take a peek at some of the data to make sure it looks sensible. Each exemplar should be an image of a character A through J rendered in a different font. Display a sample of the images that we just downloaded. Hint: you can use the package IPython.display.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import hashlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def disp_samples(data_folders, sample_size):\n",
    "    for folder in data_folders:\n",
    "        print(folder)\n",
    "        image_files = os.listdir(folder)\n",
    "        image_sample = random.sample(image_files, sample_size)\n",
    "        for image in image_sample:\n",
    "            image_file = os.path.join(folder, image)\n",
    "            i = Image(filename=image_file)\n",
    "            display(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/A\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABsUlEQVR4nHWQTUuUYRSGr3Oe94Wx\n0YKcKJVxmLEamj4oLQTX6QQltQkxAiEpKKidFPMbglYSMatomQUtSqNd1Awy9kG1iYKIoiwTIigb\ny+e0GGfV49leXIf7viF07X0TexA0CBefbPwdBIBKvpJc07TjVYMgFMv2vIjWgGonZ+MlLATFb9o+\n3fVHgqbaiek4RkNQfFv/VH6ZoKk28ng5s0QokPjW/Tfo/IYFoNqxRz9Ifw2aPrn7tpJeCJnODlV/\nWbxhQQKmb8nPqKU/XDzq/4/K6AAR/Xe3NExRlUhWo1qup6JGhnkHqJj39tea8NQdgMJ7AYhMitH6\nzPVPYqB+0L90KxE7npoB2nkrNXc//TCJILLuXFlMVsi9a3yamQIK1o0ScbmEQ0i8GUBFwc7o+dah\nV7EQceR5QgWl42Oh0dHGXC1THsbF7Pp5EAdKdj4Lm3fCs5sMXikSC3u/T+IAZdtit+vtmCgxZOON\n+mP1ucgJIKQ+59vOkqrCBatdGh0pzdrbrtUxHffG6UskazgOvzYzs0quObSy71oLnC4L6qPh4tb6\ng6t1bW4tdqDrS3vv5D8CkoplAncxMgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/B\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABtElEQVR4nHWTPWuTURiGr+d8xLZp\n6WANahw6WJRQF9HR0cmPRaiIEkHUyS46uPgXHFw7WLESKuosIkU3wVYHEcGoIEpJlSJVU9S873ke\nh5pS0td7vbg459zcR5ZGFO+ApAB55/e35XfPH30UUbCCqP24sQ1BJiu0z4wr7t7LskJpcEdtLKjI\n+yNNB0QalnXsBAEA/ME5Sx2b78eFGHEAnhhjDMHp/PEXzqcDx3B5lqEAiSzLsjxXi6tTGBxek3qi\nvAWoFkKjDLBUCB2HEFjo3rAbQUTy0XOk+OF+r2mmmo89qHQiV776HjMMjIwevTCopcWzcy4JxKxx\nKveyuBKMUBoehj9vZmdaPq2fKVSr3Ze47427LUysazr3pNmv0FfetXvIkIXrs8Lmbv32iadmane2\nbIAT9IUQggPhmuUdu11gigTPjGWZnd7ckFkONy046kX1obxqI+wvhP8yVAgdtTLwuQh6oe5S4mEI\nErPuTAQMVUsXz0NYngo5rM8kXxPHL9dVPVdfh8kK7X04ODlWVogDO2t7QnJh9dIt959Rr0zvxSNf\ntm78DqadXz9bzWePP4lL/AXj5eYzu6u25AAAAABJRU5ErkJggg==\n",
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       "<IPython.core.display.Image object>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/C\n"
     ]
    },
    {
     "data": {
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/D\n"
     ]
    },
    {
     "data": {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/E\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAApUlEQVR4nGNkYJG3tNCS4udlY2LA\nAGZT7/3HBRgfiHD//8fIwMh47y2mzv///kGUxWLKsfxnhLIYMe1kgckx/P+HIYnFiTSXZEEoY0aR\n+Isi+e8vHp1Kpsjiv67/YmBgwBGsj2WId9D+28ji778yIBuLLeARXmFBkfiD6pU/GDoHJODxJ5P/\nDBBpLMmE8aEwnqRpPu0+7kTNwKJgZa4lxcfHimknAGcEfxe/vm5KAAAAAElFTkSuQmCC\n",
      "text/plain": [
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/F\n"
     ]
    },
    {
     "data": {
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/G\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAB30lEQVR4nG2STUhUYRiFn+/nOhSN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\nyvgL2NvGcsPWftEAAAAASUVORK5CYII=\n",
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/H\n"
     ]
    },
    {
     "data": {
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/I\n"
     ]
    },
    {
     "data": {
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/J\n"
     ]
    },
    {
     "data": {
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       "<IPython.core.display.Image object>"
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp_samples(train_folders, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_small/A\n"
     ]
    },
    {
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     "text": [
      "notMNIST_small/B\n"
     ]
    },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_small/C\n"
     ]
    },
    {
     "data": {
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_small/D\n"
     ]
    },
    {
     "data": {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_small/E\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_small/F\n"
     ]
    },
    {
     "data": {
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       "<IPython.core.display.Image object>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_small/G\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAB+0lEQVR4nE3Sv0vVURjH8fdz7vHe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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_small/H\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAAi0lEQVR4nN2QMRbCQAhEhyzpUll4\nQmtzF2tvZZ8DWHiAhGRsYPdhsdY6FY8PDACS5MoZCpdi5kqSHNDRb8G4TiDSwg9IkC1MUDDp2SL1\nnLxXvGrbYhbAcUSC3YVyjWSYaN37+1jT2/20e6q8LlfTBonlofUUW9wkPAeUMCgWPw14YI+xgqN2\ndPRH8A3t4jC4lpCg0wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_small/I\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAA9klEQVR4nOWTO04DMRCGP68dUqQi\n0pYJNAEkoIQLgGho4TjhEFHukJyAGsQRQhHgABEPIUCKxGN3hsJrZx8SF2AK6x99Mxpb85tT/VFV\nybQUITPp8Xp/e3eAJMSQ5PN2/rB48ln7YCIS+/RtuGEBMNY6i2GkeWDZGdY4Z30BpkXvXaWA1zg/\nw5+a8XiFeM0luZQgmnzfFRBmGMqQhNd42ReoQsMSLfSyDiFrqtLTm/H/oGuqAJVO2AWdOhS6sTOt\nQSNrO7FwP+7Hsxa9j2iTm2CTaLDxymD5ecVg7cNpxZoXmxbApEfd/tZe3dRfs/n94pmTP77DL940\nqbmKNLpsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_small/J\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAA/0lEQVR4nH2SMU7EMBBF/9gOoaDg\nBNDTcwUk6LkDJ+A2iFNQU3AFOALFLstKCAUlmxkPhR0ydkS+FGmcpze2JwFs/N22Hw6HcWSWGDkU\nEPum/at1cAXTbTSLvoI7A1GZ2KyZezGL2hRLa4gdz6asQNACygr8mE2i4CmVMV3i29yFgqBIV8Cb\ns8Y559zr0xLiZVBVVX1IL27fdcr4FnoRAIq4bBtC6z0AwC0geddQcaAfY3pXfe2ugJVp24Yw/RaU\ntu7VmOE4m5qmsWEL2wyvn1ODcwPp87Se/RTtwkneRPNJvG0rDZSAPIVpUpqei8cv/S8EXN1fHjGz\nMDMzyzhXv6Tdocqat+EIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp_samples(test_folders, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PBdkjESPK8tw"
   },
   "source": [
    "Now let's load the data in a more manageable format. Since, depending on your computer setup you might not be able to fit it all in memory, we'll load each class into a separate dataset, store them on disk and curate them independently. Later we'll merge them into a single dataset of manageable size.\n",
    "\n",
    "We'll convert the entire dataset into a 3D array (image index, x, y) of floating point values, normalized to have approximately zero mean and standard deviation ~0.5 to make training easier down the road. \n",
    "\n",
    "A few images might not be readable, we'll just skip them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 30
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 399874,
     "status": "ok",
     "timestamp": 1444485886378,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2a0a5e044bb03b66",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "h7q0XhG3MJdf",
    "outputId": "92c391bb-86ff-431d-9ada-315568a19e59"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/A.pickle already present - Skipping pickling.\n",
      "notMNIST_large/B.pickle already present - Skipping pickling.\n",
      "notMNIST_large/C.pickle already present - Skipping pickling.\n",
      "notMNIST_large/D.pickle already present - Skipping pickling.\n",
      "notMNIST_large/E.pickle already present - Skipping pickling.\n",
      "notMNIST_large/F.pickle already present - Skipping pickling.\n",
      "notMNIST_large/G.pickle already present - Skipping pickling.\n",
      "notMNIST_large/H.pickle already present - Skipping pickling.\n",
      "notMNIST_large/I.pickle already present - Skipping pickling.\n",
      "notMNIST_large/J.pickle already present - Skipping pickling.\n",
      "notMNIST_small/A.pickle already present - Skipping pickling.\n",
      "notMNIST_small/B.pickle already present - Skipping pickling.\n",
      "notMNIST_small/C.pickle already present - Skipping pickling.\n",
      "notMNIST_small/D.pickle already present - Skipping pickling.\n",
      "notMNIST_small/E.pickle already present - Skipping pickling.\n",
      "notMNIST_small/F.pickle already present - Skipping pickling.\n",
      "notMNIST_small/G.pickle already present - Skipping pickling.\n",
      "notMNIST_small/H.pickle already present - Skipping pickling.\n",
      "notMNIST_small/I.pickle already present - Skipping pickling.\n",
      "notMNIST_small/J.pickle already present - Skipping pickling.\n"
     ]
    }
   ],
   "source": [
    "image_size = 28  # Pixel width and height.\n",
    "pixel_depth = 255.0  # Number of levels per pixel.\n",
    "\n",
    "def load_letter(folder, min_num_images):\n",
    "    \"\"\"Load the data for a single letter label.\"\"\"\n",
    "    image_files = os.listdir(folder)\n",
    "    dataset = np.ndarray(shape=(len(image_files), image_size, image_size),\n",
    "                         dtype=np.float32)\n",
    "    print(folder)\n",
    "    num_images = 0\n",
    "    for image in image_files:\n",
    "        image_file = os.path.join(folder, image)\n",
    "        try:\n",
    "            image_data = (ndimage.imread(image_file).astype(float) - \n",
    "                    pixel_depth / 2) / pixel_depth\n",
    "            if image_data.shape != (image_size, image_size):\n",
    "                raise Exception('Unexpected image shape: %s' % str(image_data.shape))\n",
    "            dataset[num_images, :, :] = image_data\n",
    "            num_images = num_images + 1\n",
    "        except IOError as e:\n",
    "            print('Could not read:', image_file, ':', e, '- it\\'s ok, skipping.')\n",
    "    \n",
    "    dataset = dataset[0:num_images, :, :]\n",
    "    if num_images < min_num_images:\n",
    "        raise Exception('Many fewer images than expected: %d < %d' %\n",
    "                    (num_images, min_num_images))\n",
    "    \n",
    "    print('Full dataset tensor:', dataset.shape)\n",
    "    print('Mean:', np.mean(dataset))\n",
    "    print('Standard deviation:', np.std(dataset))\n",
    "    return dataset\n",
    "        \n",
    "def maybe_pickle(data_folders, min_num_images_per_class, force=False):\n",
    "    dataset_names = []\n",
    "    for folder in data_folders:\n",
    "        set_filename = folder + '.pickle'\n",
    "        dataset_names.append(set_filename)\n",
    "        if os.path.exists(set_filename) and not force:\n",
    "            # You may override by setting force=True.\n",
    "            print('%s already present - Skipping pickling.' % set_filename)\n",
    "        else:\n",
    "            print('Pickling %s.' % set_filename)\n",
    "            dataset = load_letter(folder, min_num_images_per_class)\n",
    "            try:\n",
    "                with open(set_filename, 'wb') as f:\n",
    "                    pickle.dump(dataset, f, pickle.HIGHEST_PROTOCOL)\n",
    "            except Exception as e:\n",
    "                print('Unable to save data to', set_filename, ':', e)\n",
    "    return dataset_names\n",
    "\n",
    "train_datasets = maybe_pickle(train_folders, 45000)\n",
    "test_datasets = maybe_pickle(test_folders, 1800)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align=\"left\"> Display Sample Image </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def ipython_display_samples(folders):\n",
    "    print (folders)\n",
    "    for i in folders:\n",
    "        sample = np.random.choice(os.listdir(i), 1)[0]\n",
    "        display(Image(os.path.join(i, sample)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['notMNIST_large/A', 'notMNIST_large/B', 'notMNIST_large/C', 'notMNIST_large/D', 'notMNIST_large/E', 'notMNIST_large/F', 'notMNIST_large/G', 'notMNIST_large/H', 'notMNIST_large/I', 'notMNIST_large/J']\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABmUlEQVR4nG2SMW+OURTHf/c+t9VF\nKAnCRGhikQibiYGIkurbrZ9Awmo28ElUIphE4gsYBIvEZGCQlHagYqjX89zzMzzv2/K2Z7v3f87v\n/M/JgT4aBlbVzhUy/0di7xerWv06S+o/x0k2v54SQI4jF3eUZs7XUG1doZkQSfn1iLu2f8TdBuR4\nhECOQ5fSZGnm2E9DbWMXbuZJtD13yy9AKqWUmamFftTqImWyFD5Z//WbgByn7pgwfzhxtzZgWj/9\nPdnnF+6p6sPj1Z47oBmNUtPlbth1w/ry8xsqoDe3Xc4NDcPNuXTLTg3Xx34Lt+20+q5w+IfRc1MD\nGYJ5AHnVTa+9sALBoo42s2Fo9QbT6YpVDdcOkICGZTsNN46S08xHq9o5SA0ZuK4QvF9NkX8/RkAW\nBRL7vlm19X6ZKdPlTI3Y8luYt2pEnOsne26nVpdSKXLNyCTiwWaS7Elyv4dnAXv6bU9EuD4LXIiR\n1rVt27bt+OUgZa6m6Js1pZRSyuhwZEHK292oGq4e5Oyf3SQ1XM7zU7HzIgDCpb+87F/rYwYuuAAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABB0lEQVR4nH3Ru0oEQRAF0FvVM8YG\n4isyMDISs80FExMFA1PBWKP1C1T8AX9ARDBR0HizxUBFhRUDXyz4YEVwAgXd7e5rKDjVU+mhuql7\nwb8JrGGJve/itrE+CVGghIEk+bM/DlUYwxiyhZOpaKKo097AzqCJAJD7idUkwmEujYKxNBJvafQ4\nyBIUQ97aNJCMUMmv5jvGs6JZpo9rtQctb7Jzc986v/gUjWWML+3Lxl1XNNjBR75ujcDB+DOGyKH6\n2WxwBqpzQj96tBgSd6oGt32dTMj5/pWqVqarWhmuaqWowtMq3E0hu+5wz6oMpKCvuWzFJ6KqxcbM\n+7/KCODLfzy1m8fPKvEXrCqNz5Ow7xUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAA1UlEQVR4nLWSv0oCUBSHf+fcAylh\nLjZIg05KL9Aq+Qo+RgTtQeBzNDY0BI3O0ivUFoKEm62B4D3n19Ki3tvmN56P85eDBHTuV8y+S+bi\n5kx9OLarCybdA/3rU3u47bxO+m0X7ED7XgoY3DRRQsVc4YF06BiwBCTAy7nF6PFleRoAmnNpjz8Y\nfK45e5SnN2Gl7GA4Oq84gMFPq0nP3JzUeioQtUTFP4c41m0DqK5pGgKpvAneyXWjXFTE7i6nX4NW\n+TUh6M14QNBfuipJth9reOyB5fznFyzieJh1vzYSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABcUlEQVR4nHWTMWsUURRGz71vJoXK\nZlItIkLSrKAY9geILjZibEWwtExhoQhqwMIy+AsUkbSSQrCVQKKNlRDQaFSshKSJccwq6yTzPotV\nspE3tz2cW3zfvVa2AECAsbPxZmVlPZrXANbVsSft6P1LZUY+cfLc+cOD1wtPByEKIPBQlcoWBhjF\n3A/p/QWCAT7GnCqVBcFDyIIx/Um17uMAGbf0+58JntPZUKUHOJBxYxRCzlXt7WqWkIA4L7Wr7cnh\n5v/GWMD2iptJKJZ3gnPlaApG//IWqX0xBTE+QqSXhPABGacazG0wjjeYfTDGG+Aw9QZ4BOBnEooC\nxGaDeQKLrDWYHTBepaDHydOYf3+eDv7seB159jkd/DWU9edx2C9yOHm83KvhznqAjNujZVvO1FdV\nmsfBx7i3f2AhOJ13quPdYdOBR6Oneej6lrTaIxhkXdozBLxbZgqtzpmZYrD8eLEKUWAH3kG/vq0u\nvVjT33f4A7d5mVhZacdLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAApUlEQVR4nJ1SOw5CMQxzSAcYQJyE\nizCxsnOIdz5GBiTEQRBiAhoz8ZoUtRLPUywrPydgA8bzYoYOumIKzEpIZSW6OsQ8iJT9Q0cmtyfc\ngJmrXk9FyQStHsiPVGUS9FQCu7zlG+bl4RgzNyXMum6bYGo/PUP7ehWHVDm0u4+c6YSpDhmjmJE9\n/ePYoSysW9aLgq07NuQajfegDE1vQX01HwyETH/qD5IYZCzrFG8ZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABYklEQVR4nHWSvUpcURSFv33ONRrx\nBwXBVxAi2MwrCKZP2kC6PEKwMYUPkCptqjQWIUEbixRpFERiE0wnWAQnBBWdOH/37GVxZ8y9d8av\nOM1irXX2ZhsQJHu2vra6NDfDA6n18x0QmXj1rS1J8iGSXKcQaJzIlbq9XBWSW0wvPz5NGAFSzweh\n0wbI2PiapSjj+MuP3/96APjk4YIMWDxVrqTmC/v/GSaa6uV5zhv15bpsEGMMAzLeKyXt8FlJuTaZ\nLDmNqdefdt/Ocy53tVcJlLHiuZVcl3NYVcxCiJbNAHgHVUTlQC1tSMgMIBsruhelAkhnldT+xd6H\ntgk0jlzbxMc6YRY9dPYrSv/P/hYqOmVXa91yhN/eDSaSXH+f1FJjeRSrbShRElXbUEEYOoM9Kgof\nM1UmA8Jyp3U3Jrg4yeuri6Pno96Oe3GI+jVf+zLhwFT4uWmPxK7sNruS1PreGIm9BzW629duU9mw\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABFklEQVR4nKWSvy5EURDGf3PuXYVs\nIqLyp9iIaDf7AioRJV7DY3iAjUrJE+j3CYRSdESn0FhBQe458yl2z7XcG5H4yvnNzDkz8xEIu2fX\n40o/FTUsfeNkWwZyvhQMgHJz1HOZQUFD5VGv6oDs6e49x6waLMoAniUp6WIVqwsYKSpqWC4AeDh+\nKJVhES1nTTSGVLclkUiFZwh1IRAoCijLFiQu516Dd2+QJFXaaR2lGQLMANQOpUZl8K3T2SV+g0Z3\n/Q9vtrQVb/cOWFqbz0mNUSa7lRRa2+Yf/hvab7AV51GWZkxUHzu8TOoPl6OnrHxAO9+fGuz2o36g\nNlj/UTEmd2+4WoLBlVySxxlNMy14Z++gv9JtMQKfBwi1cVTN/eUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAACQ0lEQVR4nE2STUhUYRSGn/N9914d\n/ImkQaNMcBCZzECxYiZtKIxMpFKygYIkxUIQiyiSFmaLMmqRpYtatbGFixZhq1r4R0RkSERUFkiW\nEhU6Fv6Nzr0t7szUt32/9znveziQfIrWSwhowpUY5LTt+V8Ltc75UYodn/1KNt/x70pqgi/EbC2m\nbHhWgmE+3ErRP1FXm+afMCZ9FVhcb0V2GgmoXfh11YgqVu9FRlOXyypCyvEmjVJlCVN1nPvpE4OR\no+DfF3eKk/87akUH2goYmtRrtesfiRHoT0Y96EXhadp44xom704LTcVxnxJPJQJgfCuB6kgajTUo\nAA2kpgEYxvEvSnh1i4u1riYYuZkuQjPch94WDV/eiwJE0TLlrHShQfDGwtCzcCEX7X7ucl52Xv1+\nBY0hzU6G0bjYjcuEGucUkPE4D1GMjpV09joVKt7QmDiPNizK6zBV1sLHdm/7SrqbHOqHMAVkXTUW\nJ51G+PA8AaXviGgAKyAEZ35Y4rU7SFDVoBMDsJedYLkajlIkIzjxvU3Pu3xrNj8wmP0AAvYb7ITo\ntpWMaOj+iV9PNaWTc5JwzoMCcTYt9a80965pCsfQ8bBqCdsG26cji8fkrqySM05ypkPwULpZun1c\n0XFzTjsp1ntiIe2mXbJeH2hYnB5w7HrdpWxS1makODumbICy/PiZpE/sRglpnwppyYqvyLNf0Fqj\n+pvQIOpFZunh5Iq2VGUCBd0NaMCg5/aZFJW4EDsvCNmeJ291DFC272xHJF70L+IxqvHpX2K+AAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAA00lEQVR4nO2TPWpCURCFz/w8G9HS\n9KIgWFho6QoshJQp3EFWmSJVBEGwEWwUREnAxkZ9c1I8jfeBC0jhaW7xzZ3TfCOt5RmiIkhDBpG9\nusAUIFNKqCKgUh3VXpr9bqX08zT/Wu0OnwAAsf6MSaYDvS4yd1MMDoyCBH/aUHOzW1OGzR2ucS3R\n4gkw7cxL8HGe8B9DQyKY4E8Cd9dUE/I4hoq5FxNiw8WdBfeTGgSA1N+yRrPX8VIZt7Pl5vsDbeYk\nI1I185xk8N2Ji0K0dA4KRrBy+QXB0H4U+5v+4gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABqUlEQVR4nGVSwU5TURScc8pTFAjB\nwCsb4wKj/oHRxARISuISomu/QRPYaPQXIKyNblzJlp1NrCaugYWJhoaWB1FqxdTaxva+e8ZFKc/e\nzupOJmdO5p4B4rVStd2qFB/HEEAw86RYabUPS6sxsFSlJ0nP6iJEsHjOKwUc03kzM+9YjoConPEj\n3UEO5r0hhz0HuL2M72Jys0kjaY2NCYhgfKNhJI3NzUkRxvNzeat9/VAXAhDOzN+M9WS/WBOo4gy9\nl0Z9PqICqAgAGgEAwonbN2ZR2//0WzAIwcVnSS/Kt+ejCPGa3qfOpd74JtSihJ3Um/n0r9WGbFfq\nNJI0/ng4tJRXHy1dm5LT5N2raihixBPReK7TMmggCogLlyL3x0M1HZpceHB3eqr762B7K5Fg7vJL\n9uB5uhzkwFM6572ZT7v8OaeDchM5GEhS0OiGSV50erbGz3fw31JRgdmt+/dmr3TrX7ZLzUxTDU00\nmxROL1yPcVL++F0I1f4JAQjG1htndVkfQ5h+i9Y/1dvwY6JKVsmDaFATFI76ZU4Koa0gv1ZK2q3D\n96v5c+0fpUgXP71SwDkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# train folders\n",
    "ipython_display_samples(train_folders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['notMNIST_small/A', 'notMNIST_small/B', 'notMNIST_small/C', 'notMNIST_small/D', 'notMNIST_small/E', 'notMNIST_small/F', 'notMNIST_small/G', 'notMNIST_small/H', 'notMNIST_small/I', 'notMNIST_small/J']\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABtUlEQVR4nGWSv2tUQRSFv5k3q4k/\nogmRxMJVRIMxhA2C1mJlYyHRTqJNFiuLSCrxTwjYWVqnsJEgqGgTQbAwha0INlqtkn0vhLw3M8fi\nvXE38Xb3fpx7z9w7sC8eKqiJqO/72dEfigO4ZYeQZaWNGeTbQ9DEU8sIiKoL+RBssXomGsCa/5RZ\nObcIwN6zr0QQ/eGZS+cri+g9H6vzAcxCp0sGsHn8rLf7YeDBybr2dvagW8PlR9EB8HEBw4GZT60B\novnQu0pd7f8TXtuVJJXqHt6RJHldb6BjQ0FS1F6nrQZeSexWriip0kt7p4ZBsxbABJaPeQMyvI4d\nAGS8b4S3FaIkry+nWVeUFPXnnAWMH+siAxje/eJS46Pw9dNu3KxagOzv9xPj003bvG47upVuHHbL\nKvn5POnA6O6CmivZkbQTkVcOcE9SxQM2La3wDudXZtLncIP7idw76yfvES0g03/8c7S/Nh9tUrqy\nOx8ygODWXwQbd1CtLIItL95v+hhehSNxbjrNKLAszpQZQMw2P1ExdSLBHHthlVbj4U2vBe3xZKjA\nLU0UtXDk2wYxY4rcAZSHtvkLHs7uyDih3fIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAACFklEQVR4nF2RzYvOURiGr+c5Z7zy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\nb8iyTeXfXCePAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABsklEQVR4nH3Su2uTURjH8e/znDdB\nUElJQSzVeJsEsWDsIg5RqKLg4iYIXfwfnHRQECEugpNb7CY6OmiKFBcv4AWX6uKlqRcsFuutSd6c\n9+eQaGlJ/PFM58N5Duc5xwBwi4wcruzZWsy3lxqzM9MNnAwAgjNeW5CkLEqSFm8exLxrjNyIylL9\nTSeTbm8jGCSdytSWGHM2P/Ow8TVfKk/sJvXw5XQdAieaSlO9ObMRAyA59kRpM+oUzv5f6kRNDWEh\nCSGExAiXpGZtL6x/pjTqGpZ094ElgfP1MQzOqh1VN3dWYu6OO4W3imqVCayKewAmlXVUx+kTr5DB\nPawvlnHxrh+BlzD4PgA39F/vYud/uIAG42sEyQB8jGDzALyzHGB8UOO7SjU3vHYKHhzgiGKqC+RW\nG+COU1NL7aPkVsbrOSav78Qwis/V1tJxLAlmYCFxTi7rx6194Ox4pVaM1SGMbhWqMS7rQxlwRh+o\nk+rzlUOb8ljxwOWPaqd6NNo7PTn3U5mkhdmnLz5JyvT74rreG5uzvfr+37fNNH91F+6965lnKlQm\nxkrD+fa3uZfT9xcJmfgD847MpZY1ij0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABoUlEQVR4nHXSTUhUYRTG8f85545y\nc3IsaYaiEKpNteoDIQgjq0Vggn0xIAQV0ib6gCiiXauioGhlmzYRYUULF2YUrRo3Bn2Iq4lIhMQK\nrG56Vea+b4tBCLr32R1+8GyeIz3HyrffNdeK1yZfjv1iZamYfzb4B0uo58T4Vhh63Vq/iqc/V8+v\nwgCwgAcV2+j3kjMzE2h5PvSwvKTWeZQNyTZRACQgfHJo9BwBBHgXFUDxAoCvWXyn78DE/D1LFED4\nN4lVdO3h/i2JKimRgd7h0ccBaej8SKtc3dzl0tDLzI81r6aupNcq021+sH19KsLP5bzx+zMwChmT\nnRkYh8z4UgYu5JiPsjBQ1BYysDmmselrBjYtss5PZGBLLDvkRQbmv/nuqJKKzgrVUsfA7zQUv1qj\nM3YjdRWV9o+Nl+5/stRav+fp3emz6oL/yZKO6r5T22fVK6IqLLpwqSRIlh3h1u4P6lD1rmtSv091\nJ07NVKiF/Zs6eyrmAOTydeBk3JsDYEX57fjxhvrbysGLu27OWpK7kH8/8mWu0NCWfzQ8hzqAv33H\nhpMw5SO3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABTklEQVR4nJ2SsU5WQRSEv9k99xdI\nDPbGwpD8BQmdwYaHsOYhrHgDH8OCd/hrY2diKx2gsTRBAppAUO7ujsW9xnuJUniqPefMzMlkh7eu\n/msVrxL31L3LaK0hjV2bU2I9Tci/nx7QcfLIevJw7E57ADYfT7RfuNoufj3Kb75zcfEqSVnHCDAf\n3ElS9/09Bghjd6PCQgZsxWA1gAEGkFIDcFZH0MXU122tA/bbWR81zgWp7RxZYH36vHCNN6/y2kaD\n9HPCFFtbABfo+nrwOZWtDUpcQcoAni1zho41aO0u07q8CEp8oe7tN6EzILHjZrv3gTYWiweReWnb\n/jqTLf7RgOCm1Gydz5aJbDCkULYiBhPjVWwmgxBS/eNVgBgHYSrPsEDsqgfoeT5CD3dbero+fvZJ\nGfwuBdbHWG5PjiyZVdy0lv4ZsJQm8bwT1P8P9S8gDZ+0ltseqwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABK0lEQVR4nH2SvU4CURCFv1kvLgQF\ndrHR2PkcPo6tnaUxsbCysbGz83VMfAkNySKoAY3sZY8F7LI/hFPec+ecOTMD5o6/lamCTKPYGQ75\nsWggmwDOuldx2CR7D8n9Hwy0Hb8dgmg49Vtk5SfDgSVBZICWJcYBZNO5banKsXDCQDZ6tEKxe9mR\ngRZIkpZ6KVXsf6xyz1xu42q+hTcgbwrveoAF7S5rj7XsK0avlrSQBVg8R0DrvLWXN1dU5ugk+Rpm\nQSNd2mwITOFNX6b2AZvIm4YOf2oNlWU19qn3vilrzuSck5U+F6T38JnVJyQMjJNrU9ivUNLulY1L\nyy6Pi2w6JzqbpLXDlKQsfTsd7DwwZ/42vgirEZDNn5LVW+urefHvAAHmjqplAASxM/4BfSsNgaPH\nS9sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAACUUlEQVR4nDWSS0iUYRiFn+/7Zkal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\nJaH/ZMsggpZQUr4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAB5ElEQVR4nDWRX05TQRSHvzl3ANEI\niZqoBMRyKaSl9U980Ad80CW4BHfgDlyKbsLExB2oiUkpRGgVJIQoUi41UVruzM+HuczbyXfmnPl9\nA8ajYnBSSjo/Kj5e4vrOyfFIUnnSB7hcX3pY6FxvF1fnYSLPl7c01vulu6TTDjrXy6pgptBYr8EA\nJu2BlcaOTTjA271ZObo24QFCbCA77UcJIK4QfLkd0024D+wfIgDRQvz+jgxwYSoHukqdog30hxXk\n9h2gm8a4OF1DdMlIcHk6ZmykqY65eYwuroJtooWtC1ifikYHYYBYI3JwcAHbBPvXI7020AS+/XEp\nCE3E7k/AwDGzjGPDZQBOrgn0xiY8OOXX8LxYz1JMa2B0sIgHi6tZmenVp6kI2Hj9jaAD4FNoueGH\nQaX9KcGHbSJ4gDawVzgHkIUniEEPgceFyRyxGS0CSA1Eb4jAcNys4diq5GkmBzbJSLA+rWQEcCzM\nVfISbBEsfE3QWHPxotUQTeQO9xOEVpKXYKQF7A6tktcGfqR/N6erOVRLIGYNoD8yAZZZfgPYMANc\n5hbmgY5lAL6k5kvPlxgBBRavRONzjAC+pmfgRm4p2z/j1uzZc6JXWfMHf4G947GkMBgc1eDd6a+R\npFgcF48x/gO5+ufJtpLnMgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABbklEQVR4nG2RP0uVYRiHf/f9nFcS\nz9EGCRrMwKWWSFpK0CH6Bi4aNCpELS19gaAv0QeIoEGIoqHIP+ii4BDUGDRZCDqkHjjPe18Ndeqc\n9/HeHi6u33P/MUmSYc+WTsYuddSvXz/O+Np/mKsan350ChB8fzAz0VKzXlED+Y5Mkg0SU7VPEGyl\n5GbDmulWBjIP/0o+AF3LCZHyphqaZKZ9anq8GS1N5+6MTOjjWYsC6l4nW1SHO6JI1fg6mR4f5KaG\n6bo5jyvpk5wmRHOeDTt+rbpM7exSE2yXc8h0g4Dg8X840O2iJFm8LTzJ9Jkg8+5CaTq3r0oKrXUT\nTZi02A6L6mjvvNDWFpkeazbQrPfFhVm5TNtUNKE0N9azSAfvz9vAxAaZmnWl4kvXLBHAk6Hr982n\nZKA7VezO3KovBMFGGoJuEmL1uiT0rW7eObWvrBwRAIf3p9rVv1vLXl6rRyYvn2Zh5iPp53H34ovn\n9sf/DbSBqh0k7mwtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAAzklEQVR4nO3RIU4DURSF4f/cmXSa\nClQFAsMaAIeuYUtIkm6AlaBBkFSgSDUeCclgypS+dxAzCW3yOivgqpd8Ofee5EFhxMXqs329KRnB\ns1P2exHFm5NTjqIO+RFEYzia/EeAWgHZR5AEUlnr6dVZ+/JxRB+883p+eLv/bBvntPUlVQnrHKWV\n6m9G/67wH8SPh7YAFSnt5fJQobYIlvdtt9mRCWIymzWLcwIsZzSc2BuDiXgKJA462ZZEPOr09vqk\nmU4aqV9gbHfd9/ZrdfcLWTVSGyxjs0UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test folders\n",
    "ipython_display_samples(test_folders)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vUdbskYE2d87"
   },
   "source": [
    "---\n",
    "Problem 2\n",
    "---------\n",
    "\n",
    "Let's verify that the data still looks good. Displaying a sample of the labels and images from the ndarray. Hint: you can use matplotlib.pyplot.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def matplotlib_pyplot_samples(pkls):\n",
    "    \n",
    "    for idx, pkl in enumerate(pkls):\n",
    "        with open(pkl, 'r') as f:\n",
    "            dataset = pickle.load(f)\n",
    "    \n",
    "        sample = np.random.choice(len(dataset), 5)\n",
    "        plt.figure(idx+1)\n",
    "        \n",
    "        for i, k in enumerate(sample):\n",
    "            plt.subplot(1,5,i+1)\n",
    "            plt.imshow(dataset[k])       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DR4DZgTP8+7/9LV5/9xPEUlVrLS7RyCXlOS4rz7BM/dpdrCVizWFkQHwU5t+E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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10704b8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Sie23xMvyoZATXAPfWgelB4soroxTOPQiRT1prmxdxcCBr5AYiRP+U41rp/3kRNFFTg0N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\n1ekxapPvUbcuQm1VmMlIDR1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kp+nfXsv0zn9JUeFP+LhjnPqeOOZZUOMGPlkQ3VnMW5sP\ncaqimdD3hph+LcJkSMfnOcPB/Rf5hc9dpu87k/ReMigwLJvnwuUWjoa+yFDUIDRtr1CY3/E5Abfl\nBu3txRmOsXrbGA/er7Mj9A6JUAffcm6gh/Vk8GS/8wXmThD/0R3g5E5ivkjPd5vMi4qYN/9qC7eW\nd+RfLd9ZMTFsrab6O+4M9Q01HE/t52/CT9CufeEWOFGBSZBCoCQRHjCKQS7SqfaOsV7vIkmS8UWu\ncKVsI8+37KX12BU+8/2/ZXDLWl559jE6r/kIvjkAbUmIpMkFWNrGRH6dNCgUMR7RT/H51DUq4tNk\n4g6u1TVztHQPPd4CBN3Z79pbri0UMLBghuWHqJ4sBrsW2M82f4RoZKvLh3gD4g8T5u7WLGbfXb7V\nnX+uPRFoDxmtRTG1Col4q490VMX0aMjTGRwX7+QQP6/8egL0OPFYKfGImy6jnAuO1QzOeElMS8gh\n8LtNhrQGTqQrOG+UEOlQSQ55wVtCaYmELx2iZPAyo+HsNgqr3QSaPaSDfvp7nUwlAC1/jmDe6MUQ\nEItjOqJEq0sY39uC89Qk3quXcJqNUNoEiQykImAK5q77fS9gefVkvtTnv5+/2nUqCckkFPlCOAtC\nVFcU07p5I+1v30oZNazMzSmQk5gOmZTXRdrvplgJ0UQffbNb1NmWt+3Xt0o5MeYn814V8sUIxX1j\n9HqruXSlhJGeDKn+KEwlmSPcDhd4KnAUO/HXJygsjVDODBsCw+yIddH8VjfyOOgx1//X3pkHx5Hd\n9/3TPfdgBvdJ3ADv+1hyd8VdamVpd3WstLJUkZS1rEh2VEnFslN2qiKXk7KViiu+YylK7FhrJ1Gs\nKypJe2h1LLW72osUSSxJ8AIJgrivGZxzz/TMdL/88aaBBgiAGAJYgev5Vk0RnHnzuvs7/X79e78T\nW1UW5/YUNfZZdpCitMZOWa2DwWkftwJ+0lnbUhd1j2Lxbm3xfSN3zWIzaN6KojQgE3RqkHfC00KI\n/7ZZes6ZTzljidKv6wNrKrpKwuPhqu7jj/9+isCIwJ6Bp3JXvX6cmCpf7rhD4xCJctkeYUbZgzO+\njWzEhqKA7UaW5JiXQEeKkBghM6KB3Qvl28iWVxA4f56uizAxCsIA+/0+XL9eheO1FMo3LkDIBSLN\nfMSItUiYMXfdKUcRV5r24DvQjL/jDL6uSQaKG9EbHoSpERh7A4xvIZ1QCjI5ZT052dxYXFXEtI33\npuCPkxCJXYBQ19z4/HjJIIV3AEiiYyNi8xO2F1OkxGlimGJqkFmRZuiruUeQwjv+lkYmEOEXqR3c\nVO4j1h9j4ulJkuEYRjDLQo07C84yqNyNa08xdY8PsO1QN0cZ5+BsF9tfmyZ7Mldvx63T0DrMsQcy\n+IhTRoxd2xX2vlvhmfO7GZvZt4TwjiATdMx75W44KWA1mncW+D0hRKeiKD7gvKIoJ4HPIXvO/bmi\nKF9E9pz7JbQtUizCeyOSUi0eKyDlcBH3+fn0UxWIb4VwdGv8VXxusNmHbx04sWi/kRhEYgQRBKlh\nQe1pzcgl16lIG6KB25+lsXWa5pYEvosRRq9IkVwCxEUDPcZhxvUyMvoMGLIdmoRV/CzULDKqg8Hi\nauw1buqUQeqmINXixHfMj+hyoU2qkP0g0uWSBL6yAZxsXphGBhOm1VkzpBv2wH1e6j5ZyUf/TRRF\nUXaS1/oRQBJhi5F166SKnERcfmKqD5+I0WCk8QsvUnhbc0vnzyQzliUzFiXuLWekuBYi49Dbl3Mu\nKshEtSJ8pSlqm8bxbREY1dOo2524DhRDaxNiKkhyPMLNXi9D5yqw290oJW6iPSVEb/kpj9zguGeI\nBjs0CigTNahLVgxVkW1xa5EPmD+5C04KWI3NO4B85COEiCmKch2ZSrdpes6ZlbnFGor/3BlSG85g\nx1ldRl1zjBEiGMglkytNtc6cmBr44nhkU1SYOwI785kbSUq9M3xg/xXe9+AIwUgfU1dkEnUFcO7s\nbs5MfZKhQJR4uJ+FnVCWOwcdHYVpyrBTRREl1Aso3h2h7uPD2DwzTF/1kk26c+fjRrZZm94ATu4N\nmL+S2dra15dg6/Nz0XB5rh9pijLsKqkyB7EqNxGXjyQeaowgXj2FT2zJjbXeE9ZYNQOIQ3oAwpO5\nBKsk8/eYB2ilrnmaxz51jfYj18m4hwnYt3LOuJ/OjvsYPtVMyVvvwt53E1t6DCVTjaJUkblWjvEz\nPx8e+yYfLu1mfNjg7AvQOwWZpVpf4su9YL7Bx+aSKfcC8rJ5K4rSgixgcQao2Sw95xZq3ht3lHkj\nzbywm0AWH84hD06s9rCVzns++uT2CBpzoeZicW12cLtwleo0e3o5oJ7ivAKjCogaH/YaHxOTDVx4\ntYWYPo6sQWHNqFvuPGRXIg0nMXykcwuuqC5K9ZFxUteizDrMh4mCTAefc59tmvvk7cTix6stmGEi\nNDMUScMAAB+sSURBVJd2mOf6kWYQ3aYS93oI+/2yxrbuwaWlccdmcWfMKo5W67v1HsvZwLMpyM4i\nH7ClsktysUGxw0MtSXa2Janf76Z8p41MdIzwlEKifw/DV90Mv1wPF83YZj9QC4la6CnDjo/tY608\nbKslqGYYQEevctJQlWJy2s5swIGeMXeMVmVhLk1+U8mUewGrFt45k8n3gH+b08CXstS/gyGFpAeN\ncmZIESVrZPgm8BnmAp7y4MR6I9/JwWdwu4ZsbbQggBS4/dDYjlbTyuClTjo7IXgLhF1l5uEmEr+6\ni+kfOzGeeQviZiErs/rHyrsWFSG7DRHBiYahgIsUZcwyhYaKkTsXDfhH4APAt/Pk5J0H89edzMCX\ncrI7//UjzSAZHIQpYZIsUfzoug1CNhwBsMXMKTSWjrIyzySXRUo90Ao1Xjgo2Fp+nY/xIt4qwcXg\nfbwUeADjSjfRgSTDkWGY0WDEvA9nkfbqLBizMOrBiLt4M64yHT9Owwfi1D+RYI/bSxOTXHwpy5kf\nlBOdNuOiBVJp0IDv3iUnBaxKeCuKYkcK7n8UQpjtidbQr7HlLk51aWz8r7vQ9uvOaJTGwrx2McRf\nD2SpBoJmoEbe/RpNzagJ6aNZDosXotWJamriaZwujbItKluqHSQ6bfTeAMUOpV6VqcY6Bg4dZOIS\n6PZbzJtalpr/dtjQKWeWJjSKCSMEuRZy6VyCFMgMy+eRiffj5lc3UQ/Ltx8DyFSULgGN5rNaIg9e\nXgQyTIwnuXzVw7aPVxGnCIeRRcRV7FEFxeGFqvJ5n3Ouxb1q11EdOnYyOMhgQ8klknhQKIc9PpTj\nsMPfyWOz5wjpJTw/+wleHW6HkwK6epAdlaa5PWY7BcYMzNgxZuxcw8Y19vJQWQTvgTAtpTMccQbQ\nRlx0ljQTDbtA10HcRJbJvYI06N0NJ69a/m5BZigvB3ONrAeUJV6LP2fB7ny1GGBjMiz/F9AlhPiK\n5b019mtcL1gdlnm2U1sVzG2o1GZc02lKuuO88kqavW74rAMOlsC3pKzKkxOrCSTfkMbbBW61c4IP\nVj/HkcYg8d5L6Cq0lUJ9ObxwqYhzf15J4KpGNplgYRzvnY/tQmM73TxMGI0B0kCCIoLUECGKTgy4\niBS4TyCdlq/Cpu5hufFoRZYlOeKCP2mDw9fnPsqDl0eBFL66OEV7I4yTRAGKlSjCoYLfAfvaQX9I\nhuvHkbFhteCsjOOtjFGpTlLNBF7SuAlhpwcbk6gVdmgQ7O/txHM6xGSmBOMIMhnAaT2HxY5QmN+t\n2S2f2eg/4yWThA81BnhgyzA1t9pxFB+CsjSEeiHTBJwDdgKPAf/pLjh5D0s2jV4CCgrzTcfXCmuG\npbUS4/znd3usFtY/w/I48GvAFUVRLiJ/rT9AEvzdzdBzTswlo86rwGvPcLLal03zghvbpMKVF+Kc\nvCqos8nbzjcfzJQHJyqodlAUMDLkaqrmiZwQt9vA46K0YoYHS9/g0aJLnHVAj03BV1ZEbX0Jyb4y\nrr5STFbEkFtf84G0kvCet607RJb2RD8PznTTl5yiF0hkfEzEa4mmVXTtLHAJqEMakebm3DT3ydsN\nBVkY9TIQMuDXxuT9pCjK+8mLF6k4pHESFNWM6VkqstNUaLMoqgF+G/6WEqqVelnEKgZsBbaDv3mW\n4hYbTfYQzWTxK3E8JHEwgS2n7AgUakNjaCGVmObB7YlTXjSLUZMgWy7Q4k4ympN5YW2uC2tNGDDD\nFEcvC0YvqxzdZ1B2eJqyrB1PYxt2I4Qe70NkBnKs1AB/J7nKmxO4vY7L8uPkal4P7dt8iFlf1k+t\nx9pYu8Bqok1OsTxDG9hzzmoaMO26MN/max5GroWwmCM2V+x77rt5FqdCZb5ugSng6oF6YgOjbFG8\nvLgnwbV+qNsKDz0G9X8BQogZVsuJowb89TIZItoLyfE7f2cO5tM/F5td64MHm4nX19IduELpNQgO\nQcru5ZXa9/LD3Y/QkVAxBqdBLG5BtxQv6vzc8mSxJdyUnE1RJyYJXknI2IVRH1MdtcT6Uwjagf+M\ntKeabVj/MD9O3kEwo61bkQ947331lH1iB7/5u69g6RCzSl5ysdqiiF69HndMp21skH3hLsrKZ3GU\naPzK5EtUX5yQIUUVYLY3txspHJkkHiVBkS1OGiepXDPhDHZi+AlRSrC9mhuf3omShfu2n2OfcppE\nySxj+4q49PJOBi7XgtEPIsD82rQKbwWpqpulmCHU5qf//Y1oHpVGrR/ttSQTE2nSiVbgL3PfTZK7\nT/LkxNw53jkZTGCwMJFvLVhcIGFB7VtELsPSyJUv20hs4gzL+cSYhfUaVKmt5rAwScfUCGyLvp9P\nCKEp8J1I504KyGC3V2B37EQb7yAwbEcXuUdCjY3Ug3aksFo93L4SjNI2DEcRuhFGZHMFnozVZCaa\nDxfp+FErPThOtGDUVNH3D6W43gSbQ0Ur8fNG7cOcbvtX0HsaOG25NnPxLebFakvXc4qGF9WAos40\nFYPTeMfliNSol5lzlWj9U5C2dmHZeK1js8NUIQwgq8BEdRXXjxwDXrmL2SSnSd1DX6IZ15TgI71p\nWmaG0Q6qZCpV7pvq4F1dZ2QsWBvy1hWQzUA2CSndTdLhZlJUMy5qCeMnQxFxXASpYKymjsCHammj\nj4/zA9qUPmb2VtK1Yz/TwToC/e2I7ASGrqELJ4awg67kFoF1t2u2l1EIt/gZeG8D2Qpo0XtIxCH0\nswzpgBnGqHN7CdrVInePKqswmyiwbmYTVYCqy2s2zPNYdDChcJvrdQOwiYT3YvuV1Tli2aYpOihi\nTnlUMeaiIFwLeqeY/+qW+VcypVhtz6bmON9K9OCDFzl6opOGzrOEXo+jReWI6+ntXAudwNz+rRaf\nf+pZRs+fYiRQx+DWVoJFx+DWAAyOsTBLzmrHtz6QdGQPwDoapgxOvP4Ttvl7EENdUOpCObEFdX8r\nylgcTp6C3nEQrhynGZbfjZgLMGd39jlgTxPpejf9Qx10jIAehQPAzaE46htBCEQgbWpB1iTxf3qw\nWkEzQJULGrxwc8xF1zfK7nJW+VuJiSTZ54cJ9Ht5tvwE3RW70ccVjBkFmyqwtQtZ1NEMCElLf6LR\nB1nNTjZhJx4uIjrrQ8tqpIkSQydMhiiTxNBIE+YZaimvqCK5axeKp5wD77nIfTtfY9qIEcyqdEfb\nGJxtgxsCbukQj0EixnznKokUHkKUUROYpH7wZbw3axlOthKbq/ipsxqb9ZKw1YKzFpwlC4sgLgF/\n0WnqlS7SzJAguzZvSp0dmtwQbYJpu6xGZoHDewW/do3iSBxnemP9NneTHv81IcRX179h6OJSlmZt\nBkuzATNJRRFzsljFwEsShTCuuVKwS81tfS0FcwtoLWo1j0Pvusxv/LvLBP9xhrNvxflGVJoW02dH\nUcqKAPJqovr5p57h/LDO2YEdJLd9keDWY5DScsLbGgmyOG/PbnnfDTTRODnMJ177MUdsr/NmWHC9\nxo/6gWbUJ3ej/HkcTp7OCW43cnGtVCbXPEbu4edzwH3NZA5V0/f9as51SJfkAeDUYAzbUBBEBCkt\nvsN8yvORvDl5J8BsqpdGMi0cMj1+8OIVpt8anhuXHy8523Qwif7cEIG3qnnmIyd4tmKnDOpJIwVY\nOygu+X+RRoZQm1WRJ5hPSugHoY0APSBGgCCCSWCGINBNHcr2BvjIw+x9JMEX3vNlTjT+nF7auKzt\nJxFoZLDvuKy4PatLU0piHHkyY3NnncLNLGXsHL/G0Y4LaDcP8bPkthwzf4f0rs5bZPPiRN0C3n3g\nbZzvFbEMir09NKhBwkwTWKsjfIsdjrohUA49jeRKOc7B7jXwpZ6nWE/gvNtNxSpxt+nxP8t9to4N\nQ02tzfp/Mf+3sxS89RhqMenua6RVUCegOJmm9qUBHBGFHt/9OL5wCL0jg9GRASOEFCq6Zb7lzBKm\nI8YFNg+UV+OoKObotjMc23aWJucYg1+bJfSLJNm4waea4YHj8AvPfv7qe9+0TrQqTga+lcG4ZVCq\n6ziTQFTJmR4We/RhQSKOxQzU2jTMA4dusd8xSObaIL1Jg8r3wu52lY4xLxf+tpKRtxpB1COTZoK5\n7y5VPMq65TXfd1GWSfPAxEvsG4jgDF8g61CIPFBK4IFSomcUxNkBSGtII8GHkM/4JPC1vDnZdFgi\nGsy697G+zL2GgZSVVQ6occKIazvv8u+mTdzHyzPFJNO/Y6aCQ968CBAZiIQRF24ipkPzekYid+DF\nPjTTLBtHysqcRo4eQoYARnITzH9RoCCmknCmm4lJjZO1xdwqP8qU8yDBkr1MbquiuGWWsvfNUNY4\nQ3F8lOLoKJU3B6jq7ie61U50h5PWR9K0ejTiNR7OHD4KLjdP1Z7hQqiEq3yMUKqN6KkZssH/mDcn\nNcVejoqXaYhPMZ2E8Ar2kNo3zjGeniB+LUUmtLaCac2BDuwXvko26kWZZLHJm9buPqaeH+CqDWYG\n1nSoO+Ju0+Prcx+vo0V+pVjjnPAu34+hlqFdP0kmBmoGSkSK9hcHqLga5/RnH8Lx2cPw35MY55Ny\nv0gC+aTPrjC/iZwtzl4G1Ttx7Gjk4Q+d5gsfPMXVp7Nc/moWbUbgTMHeR+HIZyAxEML7/SIs1XNW\nxUnv13W8KSguBmccefdpVhu/Faad2lpj3KC9eZBPf/wcreoQF6c0uifgvkeh7qDKS//by5vfryCr\n7ULqymeAIRaGfC1O+rGG6QnAQ3k6yhPBn/Oko4NzoRRXnCrhR8oZ/91Wol9WMDr7Ie1Gdgjw5bh2\nImO95zqab6znZiNhsbYtFtymccv6KDRlZZUTjvlh2nGYn6u/S1fShsZ5c9a7XD+55JZoGC50weXu\n+Z/P+lMulbxr1V0M8w3d8gXzanK+jpk4nO1i4rzOj2yl2J3NGL6HYesunJ9PUHJ0itb6Xtre3UeD\nGKYhM8Tu57rZ/Vw3Yx+qZPTJauwlApdLZ8Ddyvmqw2xt6uXX957ikHsX36z7BBfeKCE9lCQbzJ+T\nuhIPHw2d5IH4T+nWYXiFpa28mmb0dBotY5DR1mbOax07y45gJzahoBjcbtsOZZns1WR59E2gec/B\nkh5/FniINTUMtRZ8F+CrA38d+xqucrjpIp5UQm77cnGr4cwg45lhKmOzzMZG6E7JdopxYDSWJTwe\nwX2mg0OKG9GVRhhpql1TVLsnsFVkoFoHn5BeeNMpbgZUaEgbSMgG4y4yYR/j0VuEBstxn77AQCzJ\nVIdAm4aKFqjdB5ktDfz43FZ+frGcWPQl64WtipOGmNxeq6kUnsE+CF+CaVMzNiNCzDvSFAvyvt7S\nHmXroVn2NQZI9s4yKxJU7oWYXkTnZCNDP2nmWlcNejpN05EB6vYkGb4wzcjFUoShMZ+taX0YwPzO\nx6C8MsGew2Psrx+nKDjESHeEkhjsdzvpubGNC99+DzcuZclm5lqZW843BAurS6/yPjFrp+e0f1Na\nmhuCNWOpHY0ZUWTC4vswo0SXcA+YclAselUfhIqDEBnaxTM39nEqdpBhJUQirUF2xPz62taPMHIF\nxe9Wi1ycW7CYXCEd5xoYmiCJE1QbJAIoCugvJlGm4wQIYpBgmnpGRTPVgRgltZ1cHPbw6vfryagq\nKjoTVDKCk6FYFcHQPrK7yqitG+f47nGm33uDH72VPyeKqmI3kjiyYRQjV414GegpWRUgn5iz5SfT\nQNcWPDOtMAzIZG73Vm0E1pIev8aGoabuklv8JU1Qf4yjJ27xW++5QNXslEzYGwUC0D9qp2PIxURI\nZ1qPEBXzVTkSOtgiSRwvv879Zy7giAochmC/J8uh0iyuXYY00m5BhlH5kb4+FbkIwvI4og84pxLv\nUjk34+TSpA0GIpx9UaBF5O9WdwCOfQ5evtLK//nO+zjb+TSyvdXXIY8mqtvJiedUAm9fN9i8kE5z\nu40fFodENe0M84HfGKEuGmD86ylSGdj/SUhU+vnp/zvAD1/ZTzQKTm+CHe+9xrHPdPL6/6hh9FIt\nwgghzRown6psHmve2VhVF+bxj3Vx/+5exv5uhouvwwEP7PLaOX9mJz/4xRMkot1ktW7Ldw2k5v1d\n4P3I6p75NJa1IX0WZv105uX5upWEtgpwa8il+ZlFqJm+3UWbNqsCa33MGkDd/XDgXys8+9xBvn7j\nNxiI6EToB2MSxD/I7695/Zia8t080axnb1XLF/t5rPHMgJGGVA9itBftWcicFMRJM4yCnV14bHvZ\nvq+fJ/b/mKtXy/nOt7YRz8hialnsZBA4jBpe0uvZ+0SEhw4Nsac2wF/85NW74sRQICUgpku9K7bU\nIMsVr5cL3dp+Yaktwnoe60646/T4tTfbNbdpBigGtm0q9nfb0N1Zwl1R7JMRGAPHNLhnoUyDfT6I\nFbFQe55b3AYYEdAj2BNgT8iIqSYB2QjEByAehrgfdC/MNX/JItX3abCNgy8ExQbs8IPPD8l6F6kt\nxfRObqN3YisxNczU5QmefdHG2c4vo+vVyCdMfpz8h7p2ykIT6EYE2gfZvn0nk1dLmO3xI7cbsdxF\n2qCkBMpKaS/pZ4//GgdqRth2c5KMYTDQvpOxrMrMhEak38/lnhoCQS+QxuXKMpWsoj/ixb89xfF/\nPsLItVKGr+4km47Iiza1cKcfvJVUl0TZW36Vvc03qQ0OYqSmKUumKKp2M2js5Zq+l0vhVqYiU8iH\ngHkLZ4EeZJJcOabmnd998jPmhWkjsOvONbPWBKsgs0hnux+cTdR77ex1vUGjuEaZIuPwFWQtJ58L\n1GKV5FYP8UYf/YNt9A21EYlMM3ZmirM9NfQl0oT0DmTj4QtIzSF35DWtn5aF55v3Na+UlLX4/5bd\nn5GGtEDM2NBnVHSyaDmjUULVSTZIjSoxW8fEyBHimSjSz2LtSuWCaxGG/8sAE2+co71apdc8Wh6c\nBKaf5ofJG5xHsupexVWvB8Qyf6/HsQZ4m9Lj82sY2sLCxE+Y13qyoIBjn4at9UUmp1Oc+7bANwFo\n4MtAeRaq/LCtFnzVQCWcisHx7YA393Iz15lICYASBPcYvHkL9g7CeC+MKTBug6RVaclFBt6MwQEF\nGhMyR7C2Flp3Kkyd8DL1cBlXL7yXsxf+GWd6e+Fvn2Fq/ApCbAc+LCfgpbw4CTzyFI91vUlr9ApX\nf70K/1E7nV/bwmxPPfInTCCfUkVQFIG9H+BA6zSfb7lKzWgfyR9k6G3YysyvHuOWUsvL359l+JV+\n4okych4p0hkX3X27GT+7gweaf85HHn6L179+gtEbOqTbkVuOXFlQTylUH6C5bYhP7X6RfY7zvPCj\nOGg6e8oNSneV8I3xx/he8JNMiQFkKrw1LPBW7r0dyFrNkH9j2UeQOYnt8oc0ozatG5IcepBFZ++E\nW8haoxKLBZe19EEvUqnQwVkOxYfZVnKDz3i/yxajg+vqFGHkbdOnwBN+qGxWGX+ylMH3NXDupx/m\n9ZMf4VzPZTydlwjNuIlEXkCqENPIVPDHgS8B67F+lkpQ6WflGh/WcYvnWwyTfOucVku/6dg2gBCG\neB60iPR/ag0gHkCyb9ZEMT0DV5l1PcJM/yAtOx7kyzUh3nX+LJAfJz7PYT6SneRo+ibXWF7o9SOr\n5KxGoA5wZ1YUZMBO2zKfW81pq5lv8bGt41+7w3fWkh7/1OobhpqnZoV1qy0QwRCZF37OSPMhzhqP\n4nTEQYUiQ1AcE9QoSerjUYpjSbBn+U7/BEpZpZRvbqSSavZKnUaaXSMOvh2a4nGllcm4i0nDySRO\nUqpqzSUAAVfTNwgrO6g1DKodaSoSEYrDcUJjPmZ7iugZqSA4bUcb88JQL+jXkZW8v4JlA7XqJqrd\n3f2c1e8nWlyGo8nHnv1BlEe8+JIGaWZIo2HHwE6K6bE32H+8if1No7iafATf3EpvsJieWCM3b9Yz\noBQzftNGOPgWchuSiwvOGET7UyRORwm4/QT2bKdsj6Ch7Ydsbb0fh9GFmoyhxMHQougZnb2pAIcz\ng9R4UlzTPKiNLRhpJ45IBZ3xLXQnVchqyFVqGqUNZAWPKzlOFsS8r5oTZ3sT9swvKD+4gzQuFI+L\nym1DVFUHaegdXnDX3GRl4W0uoltIT5jDm6Zu2yg7m28QJ0GccmwIVAQ2MsRuDLNlZwNFxCly9eHz\nGDzYfp3DNVewlczQ9VA9gbJKJoM+3hrop8pdT3kizWSgiLFbpfQGS5mIqzBeBONVYGjAdeST5xIy\nCud/AnOp4GtcP+ZVrmbcaudban7rWKuJzYQChDF4lZuV2/jRjsfRjSLer7zFrCtIxD+Lqmo4MHAZ\nKYavd1BRl+bMd3/ERF09n/iFNOHly0kseeE2f8NS6EOWfVsNrFe6ElZ6RFrPZbXz5TvWxFrS49cQ\nq5vzmpvC24DMm5MIMcVAvJHJPUdQDTukwDas4+jO4opP4B7pxz4ZBHuCkcQZ+iOH501/1ngtszts\nqoSR5AVuKh9DM0pJU4pGKYbdKW3epm9Mh0j4aSLqb+LOpHBlYzhGb+EIDZC5Jci8KJiMamRiZyGS\nAr0YSzEdeQ18Ka8mqon+OK+5f4XhluM8Yn+dvf4uqh6fYP8hPyHchHFSRIoiUnT+fQ+/9+h/ZcZf\nztWynVzvaeGKs42xXoXYNyZIECI1bqYpm+eD9Jz09WHMTnC9uJFA9cc50fI6++/v5F9+ro+ibBDH\nRBrbEGSuuEmdeYXifo1m2yjx3cVk2koZ/PQH6fhuMcHzRYxpKminQU8w7+gzte8S4A8XXWV+jWW9\nxw7hjb3A3j/wEKEYRVU47LvM7uxNYteuEGNeEb+TJcXUG03d0FOaZOcHu6h/spQRShmlHicZnGRw\nk2ToaQcnPp+hgSCN+g0a9Z/Q4JxkS9EYQ0oVMy0P0TN8mODpZvq/9z3+Rn0U5+A42nMRUq/HmJ4J\nwcybkEqCYSafqMgl+aX530Ry8lPWtH5+WbCGrFhjb8IIJczp9p2Mvu9hHml9k9/Z+hXG64robSnH\n4dTwEadSm+HHTw/za7+tMPuX76f71D46/voBRs587K44WcbodduY9bS4LfX4Wvz524VfYoblwss3\nxpJAkkhjMZEdB8FeJC0HkaxslpcdhZgDYh6kTbiLsWjDEvNaUQ4MMMZOpJ5WKV+qe77RiOltUn9C\nVD0GShL0WYjZIGZA0IzO0JFJCOZTwpoOnj/0cIYR0YjN7iKjnKfMNUtRa5zK1lmmqGSaCoqJ4yfK\n2POzHG0e4KLzABe9+7hVtI0btj1MhWMQNgN3l3BeGQaEw4hElJlAOzNT23h33WtU1k5y6NgYxZko\nzmEduwcyAYjrQBiKpiCWLkEvKSKyt42e58vonXYj+wWZSUTmrbO2uFkrHHXVuMIOKu+3o+JGQdDE\nJDtCXQwUTxFjqfSp5WFduHZXlvLWaUruHyGFhwh+3KRwoVGEzvRPVOrvN9hKgm3aKNtS/RSnYqCB\n7mgmWVJPqHIXI+M7mPGcZiazD6I+iI4h7boa0vdhcmMNEn87l/RGY3GUioK89jRjJeVMNh/ihDjF\nYeMCw21bUHc5cLg0SgmxJTlB5wtR7tvRR9AbIuBtoF+sxsyz8lm8XQ7Cxcf/ZUMRYmNPY4kC6+9Y\nCCFWFaNa4OR2FDhZGv9UeClwsjRW4mXDhXcBBRRQQAHrj3VJfSiggAIKKODtRUF4F1BAAQXcixBC\nbNgLmWZ3AxnZ9cU7jB1AxlRdBM5Z3v8HpEfosuW9MuAkshHei0DJCmP/COlpu5B7vR8Z+vsKcA0Z\n3/Y7y8y7a9G4315uzo3gZTlO8uGlwMk7n5P1WD8bwEnJEmPXzMvbyclmlykbKbhVZJhtMzIorxPY\nucL4PqBsifcfQtZTsZL3Z8C/z/39ReBPVxj7R8iqiNY5a4GDub99OWJ3LjHvV5cZd9ucG8HLcpzk\nw0uBk3c+J+uxfjaAkz9dYexd8fJ2c7LC2E1xr2yk2eQY0COEGBRCZJDFnp9cYby1Xc4chBBvIgtZ\nWvEkuWIiuX8/usJYc27rnAEhRGfu7xgym6JhiXkfXWLcWisq5sPLkpzkzmdVvBQ4+SfBiXmcu14/\nG8DJR5cZuxZeCjLFgo0U3vXIXGcTI8yf5FIQwM8URelQFOXzd5i7WggRBEkaMq1vJXxBUZRORVH+\nXlGUEusHlkqJZ4Ca5eZdVFFxxTnvgHx4yYcTyI+XAie3417lBDZu/ayZk0Vj18LLZuEENsG9spkc\nlseFEIeBDwK/pSjKQ3l8d6V4x78B2oQQB5HVkuYKvS+ulLjEPGKZccvOuc5YCyewPC8FTm7HvcwJ\nbMz6WTMny4y9F+6VTS9TNlJ4j7KwrEADZvm9JSCEGM/9Owk8g9wiLYegoig1IIvZMN82aal5J0XO\nwISsVHY0973bKiUuNe9yFRWXmnOVWDUveXKy5PkvM2+Bk9vnvWc5yR1r3dfPWjlZbuwaePmlc2LO\ntxnulY0U3h3AVkVRmhVFcQKfQtYLvQ2KonhzTyIURSkCHmNhRTEzz9jE88Bnc3//C+C55cbmCDNh\nrVR2W6XEZeZdsqLiMnOuBqviZRWcwOp5KXCy8PxvG3evcpI7znqtn/XmZMmxa+ClIFOsEHfhCV/t\nCxlC042s4Pn7K4xrRXqOLyLDZ37f8tm3kAU1NGQfr88hw29eys19EihdYez/BS7n5n8WWd7tOLJE\nhnlMM9ynfNG8jy8z7rY515uXlTjJh5cCJ+9sTtZr/WwAJ6UrjL1rXt5OTja7TCmkxxdQQAEF3IPY\nTA7LAgoooIACVomC8C6ggAIKuAdREN4FFFBAAfcgCsK7gAIKKOAeREF4F1BAAQXcgygI7wIKKKCA\nexAF4V1AAQUUcA+iILwLKKCAAu5B/H9aN/xqVgGX6QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113e71850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ysQm3O8AOcRV7NkAoCsnMUkvr7XSvPOCRoE2CQnYrrwf3cS7aRnih7IhaKyc1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\n7xJKKKGEtyBKwruEEkoo4S2I/wd2JaY2n6sHewAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113b240d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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J85QP3TUVp/ZyNzWRWtyp1mKJi+HEMiFlYqQgHPERxkfxaeQDuFGx/VVAI52+\nMFvrrrJe7yMdTpOZBtOYma2ccwcI124jvOpB+n1tTGZMcOmguaQ/SbfkBDedv6EMuC0bv6XhqQlQ\nxyrM0U5Gx7yEtGaG73KR9th4L47huRoutC5VWzXXzAI5G/zuMHfVnFiQgpuUYXmrolSPS/qqONm2\nE7GlE63hDXSKKoHlOARQe36a9fEcI6n1+Mc6bm61nREEovzD2VJvnMhb2uJTkD3GxV0NRB/4OJ3h\nzWxJfJnG+AIubgeUiUBxEuuH7mkYrGok479rKXc2E2qFshsWeDkXiuQmotVcPbmFTFUzoYl6mija\ntecy3zjnPwCttSO0bn7FYfG+HaDamQd5txPAJJP3alz51G7sb53D6pvGlbMKZ+qAcdFi6m9spnMh\nzNFdS7y2mqU54zzUU1GuyUHk2pOdgMYu7TD/QfsWVVMXGL44RmwU0qlSZ6IA0laAC8kdXJh8iOlw\nN4y/Bm4fuHyg5WPldVGyPoLls0kHLAbdGabxciLbSSDaQnWdSdv7fKzaaxD6m8O4roZLwl3VNZ0r\nmrSkJ9g1XpyizYUFhbemaX8NvAcYE0Lsyn9WD3wVucxDL/BRIURswavdkihqemk7QFe6DSPuoj3b\nTRvFLuzUtDTgz8cNzo0Z+D2v0Lz3F+HwTeZlhoK9kOZdJtyNLBgZpqaDTMUCBLxVHNjqoi0IsWFI\nT89s1OVwCi4X8PcJuJAAn/c1Oj/6aTi1WE7KfPbKPKRkw02DkyuBEfNgnK2l2mejR7yFYEJnNPRs\n/OjAM8AFoPbVq3zxM1XA7dR/nG1NxsZDjhGtkePaOhq1DHVcQpAtuII14MtTggtTAp/rEE33fB7e\nXAonzmuLss/V/xRgQlsLtLvw1kSpjpxDH+1jehRisWITc5ZgCTdRq4Hh3GrIXoXMCNJOEqQY+Fc6\nbAu0fNyZTrKQxmMS1ATTk17MWJodwSDrO6Qf3HltpykFIBhP09q7cChDJfrMl4Gnyj77HeCHQoit\nyDXgP1dBObc8chkfQ90dXDqxg8hYE1AquNUkUQd2Cfi0Dlkv5H66QOMtzIvSkzPIGHsD8MBgFF48\nQePAee7aH2fnk1Cbt3goW9xcjURxozzne4BPAnhs1j19RZ22SE7KArXeFs3bqRMhMysumXjOWtRM\nCuqhZJI+X1jZXuBnAN2y2djbqz6+hdvJtUA+u4GLnbzxrUfoPrkZK+stxKkoL9Nu4FOA5RPonyiE\nCi6SE6fJ8iImAAAgAElEQVTdXUVQO6OFVIodsCsAP9vA2I4QR3tdXO6SGejOTOOSwVcjr1nb0nQH\nFAMMVYyIb5ZDRfkH8u/dZCdh4HsGXV/KUBs2ue8eWNtW2m/U4FEYjqaASwtxXUlio4yxLE9VfR/w\nd/nXfwe8f+FL3aoojuBWxs10dx3hEy0YY1UFi5nz4arRshVI2CBcFht2FwTVLcyLEkhqPZh8dlss\nDVeHySayTG5ZT3TnVrJ1NSVOt/lszE733rr8+brL5M5NJ9Upi+BEKz2cUWlvi/DOC/CsARNxvBMx\nmuwcbQEIulmQIxvJiR/QbUHjlUI3uoXbyVJQOtjFBvz0vVFPrj9Iq1ujLr+gqBJY68jz5rK4Y+dp\nVcgSOClXrcrdxfmnUuuGzgCJKi9DEY1wBAyjEt/JYt3h5XFaLqy0i9glm/BRA7/HpmMf1LQVa6lC\nKUvmDtMguhe+2lK7xCohxBiAEGIUWLXEct5mlHXSDNAD2kkIjss1NbyUjufKzKVm8V5yvM/1bVXg\nMuOlaKLo9q3nK40f4ZnGD9Lnkzb8Mv2zYrgxud88qN4uM04UnNPyDDCGzz3CmtoU6xuh1i+/mUt4\nO/0BkH9R3FZgmXIyF5x3a0N0Eq5epS05wf56i/XV4NElR16KEymvyPETya+rQq4TJ7OYD6NAlwYj\nGuSKVnHnomszb0kDy0VxlT9nzmRuliOLbCdpijmfUNCtvcgF1w8gV3+d7w6SYA/Mfw5cP4fl9Y3Y\nuqlwPOicDaNZXNEUtRmT1R4YsSBulyqAKs5YA9zCZH9kTufCsuFlLLuKaPgeRnIt3Jc9RB1zW89n\ng/M8l22zZbyrklNvUTgtkCA74gQe7xj1DSlWVUEwh1zik9mFd7lPWViQuzrvBZcxFFd5fTIVg1Qf\ndR2TbFhv4RqXa9OYVlHp0QCXbXFg9OhchV4DJ0obzxcxIeCshbffpjorjRpq7gmzPD+BFN62RukS\nrWqlkkp2uynddVW4NVKr/MS2VJNtLE0NKrfcG2lITLAgliq8xzRNaxFCjGma1soC63XNnh5/K0B1\nsfwDsk3ITOI2DVZ7E9xRK21jo6liJyX/iy7gLDCdhr/+/YLdrmJeFpsef/1Rqhva3S6Mr/qxbT+B\nPhchisulwPyLVykWFSexDPyfPy58vYi28hJFF04HMrj27YCzXagohih4I9CSkVOyKdDGZ65pUY4e\n5H5PUzb8Xl/h4yX0n7L0+FsKaphSq5tngXHoiMMjtmwUau8B4ApwAohl4Y++VChkkZy84njdSWkP\ncua5AgMm5DLUTefYnBZyZUykfnxjrXFFxdDU3Ix5mrkY2EjYPYpcfbN4lg10Iz21Ryx4roLNbSsV\n3o4QAECGj/8c8PvAzyI3854Hj3Frpcc74Rj3hAm5KC4rQWNVgnU10G8BqVICbOTeJiFgWIPfCtl8\nQX5VMS+LSY+/MVCahOx4YgTMsEAXghpD5iMo96Zz/8j5hLeTk9+sg/8tv15EW3mc/GK8SM0kfI33\neC1wOqosIAHeaWgw5IQ+b5OcT/MWSJFSg1zl/KNZyK+ytIT+83anxy8EZVh0k18/D9GWxjqgYac1\nxBvFlrMR6QcY0OA36iA/zi+Sk0fn+a5MeIfTEI5Qqyfo1C2ECyZsmZB24+CMUwMTN2PuZq54IebK\n4hTeqsad+aMKqBfwzwtcoZJQwX9GMtWoaVo/8LvA/wCe0TTtk8hlAz5a6S3d2rCAuPQ0B9JSgpWt\nDqMeyVeRWlXGgAPFZU2WES9lFm07CcYgfoZYLZKsRQruSjbIUCU5OdlfXK5iGXGyAHSKSyrOWEiy\nFIqTryG1qTRyK/Q8bh9O5oFR7SK51ku2MYtwmQgENvCPyFla2oQDLxVOv46cKMVEYRw4hdffS20o\nKxNzEqAZM7XSGwULF5M00E8QuV3H3MghF9JaCAsKbyHEx+f46p0VlL9MoIbgvIalZSGQkSrTLDkr\nAvgwxWiCuywZEpZfQH4Z8eKw94sUWMP4tRFWaRlW6zC8CG+lAD6CZHCbHx6oho6J5chJORxdWy3u\nXkVFc1YBfCj/OoDUBf4ztwMnlSEb8hBfHSRTl8V2WQXh/XHkYLY+AA/UwIax681JeaONABG8viGq\na7NkM+DK2wSdM6cbqYhbuIhQSx9u6jjPjPUlHSjsU7wAbnre2q2FsmgTbCALWhp8hrQBeGf+wpkN\nlUTaz5Y/ZPKyy5vD32gRaAa3v6iZLBQy6IRpyqUnlj9UYGjeDq9TXPJ7Ac27HBZvp4ns7cE0NfTT\nQZgmzFlGu4QBfYlZfngbwsRFmCa6WU+U+utS5oLCW9O0v9Y0bUzTtNOOz35X07RBTdOO54+nr0tt\n3hY43XACyIGeAp85p/C2gW8B/wv4/ygK7+XNiwVk0b05/A02/kZwlYXDLQTFyedMSKlV1JY1JyrG\nyCG8ldmkQm/Rt5Gc/AnF5c2XNyeVI041g6xlkgYsR4rsvyIN2//dhP78IH+7c2LhJiya6LHXExV1\n18VWU0kT/DLwBeDvyz7/IyHEH117FW4VqMZlF5dBq6YgvJUAUzEIu5Ehm9+ifM+B5cpL/g5tDbLa\nzIWqK8BuYB/wfWRcryPabplyUgZnun6Fc1ZnOynb9/D24GQemLjJ4kPDg3BIq33APcA3mbEq423L\nibA0spEAyd5ajCn/dbHRLDXDEm6Onf8mQYnmfL6TEt41lCxF6kz068ifAjOmw8uYFw0svZhrUL7e\n6QJQ2YQa4C1di2QZc+KASomreN++YjtRCSplpd3WsHCRxYeJu0R4r6ewPHu5bfe25URYOrlwgGRv\nHbkp/8I/qADXYvP+jKZpJzVN+ytN0+azvy8TlGVllUUbzQUN6b9yYJny4gGqMcwQ0aSHyYTMCoeZ\nKSsLQgNPaX7cMuWkNNwLQTGRroJZidNPoAFNpV8vU04qR4gEbQxTTxTXLITpwPrS6cjty4kNZDTE\ntJZPvpx7nFJ+8YWwVOH9Z8AGIcTdyIDF22Cq4widU15JtbLkAggVXy5jXrxADTmzmmjCTSQhQ/6c\n4qvimZ4OnuIyzcuYk7I8ybw/u5JZSbmTVweai18vY04qRw3TrGWQBiZxz0KYC9hYlFK3NyeCYuxt\nTn0wOyoV3kvKsBRCOJM3/xKZMDgPbtUMSyjWS4kpDYRe3FiuTPtUqQhXgYvIaJOX8z9fDC9vf4Yl\nlOiFPh+EGsmIGBMJH/W5GSHuC5bUA5xDxqj+UT77d3Ft5VbJsHTCMXpLn27xwc0DxWwvMsMyDvyr\nX/52af3nVs6wnB3BbIbmWBQ7lUQXdmFhqsvASSCmwZ/mzWuL5+QVx+tOKupBZZPrmwobOejHKDjJ\nZpMpvUhLbdUsRZRjSRmWmqa15hePAfggMgF2HtyqGZblSe/5z4QunXYJCksQiNIz2IDMkh4C/ksA\n/jG1OF7e/gxLpwsWCPihtYmsHWPc8lGTq7xuqqQNSB/vsBc++9Pwx7+92LZyK2VYwoxIpEVo3grr\nkUPQsA6fa4e/uLLU/nOrZ1jORCCTpSEaI5VM4bLswl6Nm5EBO4Mu+I1d8MevLIWTRxdfofLI4JsJ\ngdSGouSFt1aIe5fvZP/pRO4FtxHp0J0PS82wfEzTtLuRNPQCv7i4O7nV4LRM+uViNBmvVJeyxbOc\nCdP/Sj6bEHhHcY2ZP1g+vCgXrHztXWUQvC9ByEpgvmqSjhVXq1jIbKKE91eQWeOZDOz9Axf5gXoZ\nceKE0leUvugG0wPTulSNZtGeyn/twsGJDQ8UV4pbppzMB8WVibzzIPqoG+8xC1ePLTegyaOQYWnB\ngeK6VDeQE0e2gtDeHs1bNZTC3nmC4t9S7djvg8YQODeBnQ1LzbD8cgXVXYZwAVVg+yHjg2lKhDcU\nSf4JZDPd2gkP7ob2b4IQ4hM3ucLXABVdA2DgX5ti1ZPDNOVGcV1JY14tLn2rFtKfC0p4/yRSXw68\np42Gz27j5x99aZlxojDbjMwDhg+i+c/T8ytyit2PITnZdhc8+BSs/YPl1k4qgXOtzSxy0h+C/gD8\nSJPJ7o542k+Qz7B8EB78eWj/2RvNST5eX7jePrPJIrLdfFVQ38aCwvvfeYYllGrdbqAJYbeTTtQQ\nC0Mmb/h1PnMn/1ajTmZXWQTvLY9yOz80izD7xFH2iGPUi0jJdG4hOLNOXUA028yJyT3z/6hSvG12\nSuede4A6RLYBY8xPbgCseLF6c81KVF/VgWytj8im65NZd+vBOdgJ8NRCVSex6Ub6zruIDIKVK7YT\n1VYmAk282XrPDayTQt4FKDwyFPbtMJvoyMiGJqTNKA9nG1G90tUIvgq2gK3EbLIWmaDTgrztvxRC\n/MntsQ+fRun02Au0Ydv1xGP1jKUgWWbzdiEV8n9BWlW8lwQ/uVuaH5YHJ6q5lK4quGZyiKdOX6LN\n6OfS1ARhSldpng8W0pT3LaSbwHztKtWuvfJq18LJkkJdbgR8wCrsjE52KEDaBWbeIbBQ9WJI00n6\nuImvv+hFWB5tpVI41Uog0AK1Oxgz3uL4gBuRkUsmCOQsbhJ547GDCcwrg8VSrhsnShSqJ+MBgmD7\nwNAqW477esMFNCL9zbXFWirDpRr6LJDbdD2ItC/Ng0o0bxP4DSHEDuA+4Jc0TdvGbbMPn2Pcc7kh\nVI1V38ikFmAgAdO50qVg8/Eo/BjwS8B/eqSFfyiuKnhzOHEO14UPVBq32/Ha+ZnzUBEdJt5mjZq9\nAepXG9R19xI604s3mizo5c51XOaqhnr9AR/8r2b4+Ob7GH69QMoiOBHMULUXqsRcKFdpFv1jx3/N\nB55Gcq5mIskAo1OQypbWuBxKqKu28ulHNvJTf/IBWdxt1X+gJMENqGq3abnfRFtjM5ISTKUBUVxc\n1w18APjIjodp/fSfAzeCE6cVWQlvL8LUEUtpT9cIzSXwNmSp6ojjqS06ALSyA2Cypo5zG7YvWGYl\nGZajQoiT+dcJ5GZOa7kt9uEr6+EeYDWYm2CiXjqapii1flrI7LCW/GfDbIS1G1WBN4cTZ7qnpj6Y\na3NUf9nho7iqkkVoh4f2T9XjfbyWi2EPZ07D9FTRglnwr8wCZ5cNAXc1wc7tsKMuTkCrU6ctkpMy\nXda5PeFioChZ8ubFDqnv9kBVHZlQPQMeH13MnnLshEBqPUHk8t+DyTUcihSW67hN+o+CaglS2225\na5hd/+E4rff1Y/vNgu6rNtpdhVyFs/GqC9fXCmGPN5ATKbyF7cM2NGwLxE0W3rrLIlQfo3HtKMGa\n0tW4nL4TgG73er4WXPjWFxXnrWlaJ3A3cBhoce45p2naMt2HrzjmaQEL78Y0gfVxskaOiZGi7qoG\nayVW/BoEdbg07id8tbDl183jZEbjc3pDnKrm7KqntxF8LRrN20w62qcIXZ1mYtQkNVRsFAtlVqox\nRJ1nrqolvbuJ4W4f6eR5ddoiOCnTYxeZLFUClekgN/FeJJymJR1Cbtjgx/AEiPS4GB2TVXIuJVr+\nOBQnfg0CGgxNVXP5YME3chv1H2ebkyPlGt8YB2p7cAd6ELpR8IVAPoKpzkVNi4eQoZM7Vdgb7gZy\n4gWqsawAhqFj2qXC+8bKcdn33JrBmuAAmxvCrAqMFr5RsxEbmWpRG4QotRwfWDgXpuJmrWlaCLm2\n/K8KIRKaps3WXpc1PCGDxr0TtB5IEJqYhhOlOoUyldlAsxvavfD7p47QtvlXGJj8f2H2Pnz9YVHc\n97QQdqRy+XOUxgKXC3NpRqnd7WP1+72sTffS8e0z1FzoxjscKchLWFhelltvLrZt4dA9j/DVr32F\nQNv7MAb+QVXOico5UZXJsfj0AA/SthhgCeHi6qnnn3Yr6E+aUJ3D+paNMVa0V6r1u5whX87hcpUL\nNnshMTZN5J8+L2/rtuk/SrUByUQICNF55C0ezx1m/PIwV1O5kowCG7C3BRAfbCAzYjH0Z/8PcKM5\n8QP1mFYVqZwLS4Btlypj15/80ll9gAx7OcaTjBLmChENXEIOK1lkG1rdCFvXgzDTvPHM6HyFAxUK\nb03T3EjB/Q9CCLU90SL2nLtOGZblxqHrguJj8/mydLT3svkOk4ZGeTtOpwIURWSXX+d3s4I6t5eJ\nywUt86bsYenTstS7Jkn5NdK1foymOnB5Qc8ncQgzz5MAl0BzC/xahoCWxp+08Sdtmtv8tO6sovXY\nEC0/Oo73ymjh3ioR2k57t88PwQAczui8+Nv/iF3bhh0p3Poi2skrjtLXgaiTgtu5mSYVdjQlvKuY\nsZzfwijVvP0NaRruGWZ1QxjfoSQWRU9CuflUtRclEHqCOke9Gl+dOIzHXu+MmFtk/7kVMywVC1J5\nCDZoVNV7WBMJ0/md01i5LH250oHNC1yw/Dx7VOe1l99A8zY6w3EX2VYUOpnZg5xCwg/UYYoqUqZe\nMqOsXHA7/UqqbTgxW6S/ghevlWHLZC+P9L3FsViOiCgd6AVwyRPgYMLDwESY/qHvL1ijSjXvvwHO\nCyH+j+OzRew595ijmkvMsJxh570ecLqVBNW5BPeOXeSR7kGGYz0lLcepY9jACwEvu++uYffep/iX\n79zNaPc34CbtYdkSHOHeVa/T03on5+5dy8SabVDjkkLKtkHYkiePQKs1cdfmWO+5wiZvF5tO9rH5\nWB/TI4Kxv/TgHhjHPRGfNbJ5LihTstLz162BDdvgb8/1UF23leTqn8aYiCB4blGcyAxLBQ3saUjr\nMmTD4SCcTVtyiltbVbIWmfJZEN5KtC7UBp1XsWjTh3iH9zIbfYPkXN3zdnYXUkCZyPFmza5aXk9Z\nbG5fxeTUx7nQ81/VqYvsP7dahqXTz5JFdxlsfnCUXe8ZJvTCOEf/zSZhglsUVxUIAPWAObGF74/m\n8AW2Au8kHf8tVegiOHl0nro5485BNoAacgSJ4yqYKhYHN6UrjgjH//KYI6WAmPn/TWipBnyvnqRq\nMovnmLy6sggqnwCNu9Efexe58Qbsl9OQfG3BGs0LTdMeAH4KOKNp2on8tT6PJPhfb9Y+fJou0D0W\nmm4h9Os5yRGgaeDyERRxtg9f5P4LxzgcLh321ZjhBQaBIxMZWmsCvPr1Z0mEn1en3RBORNn/RjHG\nGg4TDOlMrFtNqrZNxo+GKNp2XIBPoK/K4V2VYp2/hz3+GPvcPewfPc6li1nefA2SGWeeZWkE+Gxw\ndlmVIV7dGGCw2sup3nE8LkHu/O86f7JITtRg6pGhXQmXjC1bIDTPOSHTyFewQUCdgICg9O7mG/3V\nd+o3HpqtER5Mvskd3i5OG2kGHGeUw5n6ZAMnvAFePDhMwxoP02G5TXV+o4Gb2n+uLxwmAc0NAYFW\nC+13D3Pvu4dJ941x4fs2XksKbDXI+/0+fFUBTk+E6E3+ENiE3KbiRnDieM66F/QQOeFn2tLwshjh\nrdqBWshdufGdvdKmdM6q5l5ZNJcLT1UVwYAP13kv9gVDbpNLkRcz4MeoCnKxbiev+d9L2GWD9taC\nNaskw/INSi0HTlzDnnOLi+NyeUz8NQkCeoKsx7jGFVKcXUyALwBNnWQbOum7dI6z3TDRVfoLZdts\n1eEuF2zd9giv7fkAF8Ih+o+GMdK/gxBikhuwN6HTDg1gXpwi++UrBL0p7hy9ytpMXXFrLqUI5JVM\nrcrCFTRocw8j3MMMdo9iXzGJRsAyi9qqswnOJbhVkKFaY2cVcqW84cheeprfyRP7ghy5mCUXVw3+\nvy6REzdQD1Y1RKvkRSvYCblkMlsl0DpNaDbhiPp2obbmDK8USLV9FXafi+xXj5PxZ7AuySGr3C+g\ndHpVhypkWG9uZBsf+en/myuREN1Hx4ilfwshhNqeeZntY6nuUP23wZuF+9bC/a1kM98n8Qfn0Y5O\n4DfsQiSSjEGBwTs3c+7J+5kY2kLDC59ictgGJoBfubGchIA6yKZkJJXfrEB464Cet20UfEoZx+Gc\nnzrDW0tieAk05Fj/zhE2bzdJvRrl4GEYz0kdXgnv4T27GHjHg/QkNpA+2w09WYiVbfEyC5a0quD1\nQblrpxTlAsTtyRGqnqZKj2FfV+GdRfN78bR1oNXqDF+q53y/XEPGWTMTOfWr9rjZ7HdzqbuNk+d2\n0mW7uBnLSzn5sK9Mk70yTZCrbOPVRZUznD+UtbLEkcTCGrebokmgWnezTvfwVmwvf3vl5xhPhCF7\nkNIAy8XAGZdbD5aOHa3CzEoLihOzad5KcGqAFrRxdeTwtGWxam3sWSNxyqGMHgAWuiuA270WVzhN\n7ttu0na2RGg7TTeKTyWofG4XDW4XZ66u4+DFhxkUIAMqlisUw45cAU8WrdZE39eM5yN3YH/p30h+\n6SIB28aPY/MSt47t1hm6cxOnPvbjjJxyET92AYaTzNii4rrWN49qYA3kIhBLSKXFKT/UcyzO3ARe\nM0vASIOWBa8Bego0gSCLTS7fX1wIoSGEJnegsjUQOtgu0DQ0j0mwJcvGd4yy87EomeEoR34o9awg\nkHC5Sbk89N+1ixM/9RNMvhwn893TcKU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45iE7gQ+4Gf3QfsYf2cssboL4aCBBA3m6iZC9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\nyQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108f4f2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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OHLK3IDtTWVL6rg4CQRfsyxEShQ7c4SgUdKfTgtJ/RTV0J9VdMADFKSheY/iYxQuff5gW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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113fcef50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0KQSmlZxqdawYTQj3WVbywdmRqFSDzNg6cTHY5z5zsqwnYKcZtLJYVnUxhnx4\nHHgalTVvSSutSTmpVZQaYQn3ub9jSJg3YHIe9KvQqRvsi99gt/8m6S6B4RNl36FEkPL6SOleLrY8\nxsXmQ5zrO0xSaphbDxF6NUTmDZTy/tPF06rAi6247Y6WnRHOyoksyUa1lf3FMpFLElNhKW+tgKpX\njCa0H6DmUJR3eoEwnQkfgfvOiTMlrLAP2JMW2ejd9cUusuOMSz4eNSkntYpyxry/KIT4HHAa+Gfr\nnUTG44OmNqjvCzD0dC/nH93KxNQWpr7diRm4h1l3j3IFUwIZ3UNa1xllC6N4GGGeeQZITE1gXl+A\naAxOvO08rYK8OFPjQHa2tLAgpKQMcC7yPt6c+gDvxVKWUnaa6Pn4qXiDvq+yshY6gS7Aa03iFqtC\nBKB71Tqg3f0aqffpGLM60rtqRevDybKegL2Ql+12cj8sb2efZ4ms1bScgGNuowJ1Oaep1nPC8v8D\n/o2UUgoh/h3we8D/WWJdJcEbgLYeaHo0wPnj+3jz8NNc/K/v49JfPgqpdyD5DpUQTHvcPI3H2kKk\nuYGZ0ZCpBYhH4ftv2MUrzIszy5i6G9XgnBZ4/teeMAOcjhzj7NTnSUZPY/IuS3OB579qBXHfZWUt\ndKEW5goK8OhZD5xCIQT4AuALQnqHTupJL6mbOtKX95T148T2fll83aZ1wJaD+2F5213FJah5OYFs\n/7cSbcReAqTUue2SlLeU0ulb8YegvOzz4YRjv5/KREMJofLKa35But5LrCHIvGhhOtYJ0Q6ItFE9\nP2+JmoS6AoQh8j11tCheTjj2twM7WJpF2BZuZ+OyZ+ftrD+NoPeyCS+Hw++wU7tFU/I2NAaIHupn\n9sAeZjMNLHxrEC7PWcvmrGx5C6FWjy/PSWKQ3GjCYjg54djvpzoRlmANAQudke5DDG85zOREhNuj\nIXxmCt00is7gJACPYeKJS1IXBKlvCE6e7iUW91IuJ1BpXuQK23pjkNripHBUdH6EpYmIp2PjxC7/\nZ0hIiA6teX5JEZZCiM1SynHrvz8PvLfayccLvEhRsIMPTVR4PBmELtX4XqIa4fG5wwx7gQ5U5NxH\ngRNF8nLc+mtHUzo7UYbjmk4Lxanc06BtBt9BeuUUf2v+uxxJ/Q1X4/MMtNQT/vgRJl54muh/M+FP\nfgLhhGPNs+WWt/0x1KU1dFAS+smNJiyGk+OlXrYICJQfjV/TGe9/jp88+et4zozimxpES4QhE1PR\nusVWGk/H5XKbAAAS4ElEQVQjEmnkiTTyrEEo7CEa9lIuJ3D/FtutHvrZqJxU0rnSYKkZ1RHcTPCR\nLxKfkxB+A+J/ver5pUZYfkgIcdi67iDwD8p5iJLgGA5Wiw1o2c+iuRhJU8EL5lopf4ESviURlv+h\nOF6co13OlVfs1+kn27nyOMol1XXbdNjZSHKfyeTBLdxO7+Hq+SDXJ1uZGdzB9GkfoetRGLWTMGSf\nw7a07VWcRDOIfhCmAYMRmA1RfOzXitGERXJSXdgu8T4JqQXB/IhOZt4Dpo/s2o6lpPa05G02rbbF\nd1j7nKw/Nh4nto9XmxfafartlJumMaCrbSEOszFoToMnQXZUaw2UGmH5taLvtBoQWU+IjNRUPuYU\nYDqXCasUcr+3n8k59q+RUv5y4fXZFrc9DGJ/h52xxnWomOY6a7NDBKzooE3AB2Dq/R28tvNZzk/v\n5tbbmxk5U4fxvWFSPxknNWnbmkslQnNY2gKgE8QxEGkDwrMwO0O2B1AoVowmLIKT9YEOeEyJPjgJ\n05chOg/GFFbKKkq3rVYajtgYnKwvNh4nttPOtgAcbAZ/OYPVFrSAWoV+YgrGknDNAG8UpboKsDs3\nboSlDbFoS4IUOZb3/ZhJLxT2RKQX6MBT56Nj5xTtvdO0zC3QPBeGUDMsNJMxAqTTATQ9jcebQtSH\noXGO2IEYc4c7SG7t4d5cN3cGNjM5rzOfSsAIKIWdG3apzAUpFU2mpWMm67p4a8su7hhbma5rQn0k\nHlwIgEgKImHU2GuCyvbUXGx02GaUAJpQ61gmzB4upXsRGGBGrSjVQpPA5MDq2M6mYVrCSCxIcmJU\njXkba6/tWciwSS8qQGcTShv+oZTy92ttzbn1RQgVdBFFvbijAEWuw2cHSPhA7CbQ3MneF05x9ON3\nOHBpgH3vDcANLwx4iYV1YlEdv08SbDDRew3YaXDv8T4u701y2TjKlVceYfS1epK3r1uXjpL147Ut\nyuy1TVNgpCFtqjQXA/oufhj4HEN6DyPaHVZYEWcNLFSAk/WGTnZoqtI+z/bwV66sKNQ2L9VG7XOS\nuwBxJ2oB4h8nHuGVzCeIijCIEZQGtmWnyAlgaywmlYJUBmYizYRGrlgd7MiapxdieaeBfyqlPC+E\naADOCCFeAX6FGllzbv09+zXgY8Bm1Mv7qv1DwevwiUYP3q0+fMEgwZEQHcTok3fpM++yVQ7RJ4cX\nxSBmbX7Ughs6gAQtlCB0S2NuOsnUOykyV5poariItnuMYe82xuVWmIrDtL3MSwYa66Gpi2iyiYmI\nTqS9hemeLob693N2bjvjs60Qm0YlqSzGEi2fkwcTubz8e4QQ+6ih9rP+qH1ORM5Wh0oAPZfZzMnM\nYRaYRalPZ6aTMqczUxJS0wUXL2TMexwYt/YjQoirQC81tObc+js7NVgbqJGwDlSu1MI58XR7afiF\nLbR3S/q++Rq9b1+k/fvTxC7NcHtugbl5MENqyxhqtSUtBZ4wiLvALMRuLjD/5jUaEhM8MniWI34f\nBw7O4tvXwDcaHmFcvh9OjMP0mHV/CejpgAP7mB2+xLWrXsL7urn5ix9iPNFP4vV7cGUMxpOoz8Ta\n6+hlYWcnLp2T9Yc9h2D3TKoxR5IrK0CNtZ/1x8bjJOtGYHuyORcczI3FKPcqhaGoMW8hRD9wGHgH\n2FSra86tL+axvm1QBCe+3U10bE3Tv3mCx3a/y9bpd5gWHYxOtjFqtiB9OvomA32LQZAo9cQIJBME\nYklkRJIehdTdOCniBBinG2jvgoMB8Df1cLppgm4ZIlg3S1CfwuyaJdM1S/JQhNThNKkLbVyaeoSF\n1s3c6jrI5LCf5M1xuJ5CJVHyUvq4d2mcrD+cAUvrkS5xMUzebT+LqH1Olk5DL8s8loNyxgGKk7+C\nlbc1ZPIi8BuWBZ57pfvh7Q/cz4QIKeCbwAuoobplHOTlJPh4N9vefZVDxkke33mHrqcaeMX7HBf0\nZ0nEgyQSAVrr5mgJzLJL3KSXAXrvjdI9OEHmcorQeZiaUSoybF0oHIbrF8AzMk+j73sc5yq7R6Ps\nrIsS+6Ag/inBZJfBREeISV8Dp6d+hbl5g9CfxYhOhTFG7Rx7JqVboqVz8mDD5mWxB+vysiE5cbry\nOo+tdLy6KEh5CyE8KMX9Z1JKe3mimlmb8P684dvAX6NGwhatzII58Z7/MyZuXOJs9C6t9TqbP7GZ\nGd9Bbng+SDRaTzQWpKt+kq76CZpEkG4yZJoFXpFBjKfB5yPl8RD2acz5BKYPpK5cjsQYmOY8PfI8\newQc7BREdjUSOdJAk2cSP5Kxhie51nyM0MgEnDoNkRDZYKFSo+/K4+SEY7+f9Yuaqy4GUfEAl2Dp\nYmsPMS+DbFxOcttGJSNWB62tMBRqef9X4IqU8v91HKu5tQnXFxdQUZYfs/7/GhTByf/9fAsDL3yc\nqSRcGprk/O+luKbrRMQNUoYGaUHEG8P0RsmQYphOWhd8NM9swRxrIRHqIdLYSmhrkES3F7kZaEGt\nYq6BTIKegMtR6IhD6o4P43d9hOUUYTnF+OgCicEzMBOHZIqsv7kdxrL+nBwv8aq1jX7gHFlevmL/\n8BDz0o/LyUroZ+ln6LVVSxfiKvgM8HeBS0KIc6hPy2+hCP5mra85Vx0MoayGLuD/d/5QMCcfWXid\nO8/+Mue1I8y8fofQtyZQlu8QqjuZWvQyUevotKGsFAlsBfZD0xboaoJdfpUaZTPZNRRiqBVTZ1D2\ny7vAKSB9DuQZ68crLM1Q6MwrW+xgVPmcPJhYzosQ4gUeal5cTiqBQrxNfkL+WKKaX3OuOugD/lXO\nsa8gpZylQE7Sswah78wxNzNN4rLtbmSvrWFHX+bCDj6KADchPAa3/DDnUXl+GsjqYQP1DYijFPmo\ndaqcJLt+jHPBrHLH68rn5MFELi9fwbFCzEPKi8tJJbDxIyzZmBnc02GD0OvzzF+fIesrqpPVvisp\nbzt8PgLMQ0Sq3TvFXNmZTlZnyaIOLly42DB4IJT3hlQ7fRloSJBNZCBRSny1hRKcVrKd6zvXZSlf\nqK7THS7tOP4ArSj8gMIO03bhwolSwuO/KqX8g42yYGh1kDc8vnBO+kyH8raDcQtMJ7ZoNdveIc61\nrJ2Bvc660mR9mquRwyNvePxDLCewSih4wbzYzpsPjgIvnxMXpYfH/9D6rSYWDK2h8HgolJNgA3j2\no2Ke7qFmFu3hDGd62NUscHvfVtia4//22lc2nHUWB7V6vLkGzxXg5IFE3lBwKJCXch3Qag/lc+Ki\n9PD4HuvnmhhurqHweCiUk2ATeA4A70MJ8DRWpmnr/2s5/Tst61xL3B4aKTYf90qQlvJWf/Mjb3i8\nfYMPKVYMBS+q/dRybszSUD4nLpYG5a8JR3j8SevQF4UQ54UQfySEaK7wvW0QLAkFhwI5ObftENs+\nMcxH/vbfsHX3MCyu65cvXNtW0F6yCzTYr89W9GmUxZ1rdZcDK1+60JEFL+5YGieVWth1NTjXLSpK\n+CuCxVDwotrPDpSqW+3enWsrmtZ+HeBNgJhHeRzV5FegNE5cFCG/ueHxqAVDd0gpD6Na6kPY1XGG\nggNFcPLWjifY9dlr/OwXXmTnwZsstZZzJy2dzdbH0qVLc1fisX0EK6O8JSwqb7MgcSmdk0ot7LoW\nbEW3vsp7aSg4RfCyT4NmFDdelATkU96gJMGLylDji6Oy+8aowbGX0jlxUUZ4fC0uLLu+WB4KXgwn\nP/61v+JO5xRNhNjUPc/PfXob78UeZSCyB4YNGEmhrJIFsoo8dyxcA+pA1IHeAsF6FfcQTMHoDEzP\nUVyOElt1Gtg2nEiC566J71IaPWqq+dV0vvPL4+RVx532Uz05Wd8sFIOsFApeDC+/u72dhckwyXCK\nXcAult+7vUyXCWQ0aDyo03VQp+XWLNrAe5AYBWm/uPs9ij5IuZyccOz386DolEGKCY8v1PhYFh5v\n5R6wseqCof2ocNbjrE7yYIE3A3DDoUBWt9YKrbWYqw+SDQX/e9jBusVwkhztoe2TP8fj/+5n+Du/\nLvjiP7zA0c8F4ZNPw55jwBFUJGXAup49LJIiOzQigCBo3eA9DEENjjwHzx2D3l6UjbaSv/hKz+qc\n8DRQbou30GIS/9U0wTcNvLdN5SCw4lD6nbI5+SBZWenPV2iVJyi0nD04tfSTVoyzfDFX70d9hG1e\nFIrhxfe3j/D+w70cR61SkOuVb+ecvkNWebc+52H3lwJ0PDuOZrwFqVsgDevMWxSuwAcLKFNsuX7K\n5aSfyuuUQsumOVPhGu2y/WSf6viaZ5QTHv9LhS4Yat/WWii0HCxV3quLYDWufoE8oeAFL6K6cGGA\nW6/uwRNppc6zjx5vjNY+wZM916k35qj3ziL1IUxtmOs33mXP3jhSLA1bF2kveiKISE1CZp5rMz9k\n3xFBtC3NwI0IQ9SjtK29FqXtMZzvWe26M+DxgT5Dpv4xQiMXmXp7nNgmqdaDWswX77Rhy+dk+yY4\nNQOH0is7NDrVzTCwe5Xf7b93rSe1M7b4NWhvg/ZmaJwzYda+/0GUaiwEgxQuUxp5QsELbj8nL6ZI\nP/oknYEOdo0P0jk1SSwEiZjVCRLQ6oPzAg7va2Wqv4WRjg4WbnVwd7KTjDEHpnOI7XaVnrXQcuVz\nUum7KqasUt599+nqWZQTHv8Q+182kycUvOBFVI2wYOjFJLPv1HGt/+N07Wpi/5OX+OzBP6e37R69\nx+5heBOkfXG+9rU5Pv+rC9Ziy2KxDXoi4JsReKY9MB3kD1+f5FeP3OGet5mvtzzOEPtRzTvGUst6\nJdi/A5jgbwR/B6mOxxmbfIdbQzeY6wJasNbHhKVBQ+VzcmwXvBqBLWk1OmMb+M6Em7aq9aIm5Fa6\nE6cSdz6VH2jxQt822LZH0vWeiZgzUCu/Vwt5Q8ELbj/jdxo59/Of4MBHRnn2zW/x+OlJpgZgJqY+\nzaYG2xvgnCbo/UQPcx/fx5lXdnP5d/Ywfi+EkZqzanIu13U/Zy/L58TFAxJhuRF9i6QhCA9kCE96\nGUvuYLJxM/vlRfZ2XGFX8C47u4cw/GD4BT94BR4/FsXUNEt5qyf2LmQIjKXxjkoYgf91Dt7fNcpt\nuvh+YCdLh03szvVq/RTH77oXvEEywc1Ex4PMT0DS/g6ElzxJxTjZ3AIBXU202XdjX8E52m+ydIIO\nsjGntreFU3nb9diWd1MTdGyB4F0JQoK832PAqyO64GVy0w76DjRQP9LMlltgBrLJEjJAmw/qNUHj\njga0JzYx/so2zr27F5m5g5o3KSXZmItahpCr+u5W4ALLE6w/sJBSFtQ6XE6Ww+VkZTwsvLicrIzV\neKm68nbhwoULF5XH+scpuHDhwoWLsuEqbxcuXLjYiJBSVm1DhdldA24Av7lG2UGUv9k54JTj+B8D\nE8BFx7FW4BXgOvADoHmVsl9G+UectbYXgF7gb4DLKJ+lf5Kn3v055f5xvjqrwUs+TorhxeXkweek\nEu2nCpw0r1C2bF7Wk5Na1ynVVNwacBPlUOoFzgP7Vil/G2hd4fizqHwqTvJ+B/gX1v5vAr+9Stkv\no7IiOuvcDBy29hssYvetUO8f5Cm3rM5q8JKPk2J4cTl58DmpRPupAie/vUrZknhZb05WKVsTslLN\nYZNjwICU8q6U0gD+B/DpVco7XXIXIaV8E5jLOfxp4E+t/T8FfnaVsnbdzjrHpZTnrf0IcBX15cyt\n9yMrlCs3+1kxvKzIiXU/BfHicvJQcGJfp+T2UwVOfjZP2XJ4cXWKA9VU3j2oQDgbI2RvciVI4IdC\niHeFEL+2Rt1dUsoJWExZ27VG+byZyhyZEt8BNuWrt4IZFYvhpRhOoDheXE6WY6NyAtVrP2VzklO2\nHF5qhROoAVmppQnLZ6SUR4GfBv6REOLZIs5dzd8xb6ayFTIl5tYj85Rbr+xn5XAC+XlxOVmOjcwJ\nVKf9lM1JnrIbQVZqXqdUU3nfY2kCgF7r2IqQUo5Zf6dQayQdW6XuCSHEJlhMZjOZr6CUckpaA0yo\nTGVPWOcty5S4Ur35MiquVGeBKJiXIjlZ8f7z1OtysrzeDcuJda2Kt59yOclXtgxe7jsndn21ICvV\nVN7vAruEENuEED7gs8BLKxUUQgStLxFCiHrgoyzNKOaMcsaq5+9b+58Hvpuv7CqZypZlSsxTb1kZ\nFVdAQbwUwAkUzovLydL7X1Zuo3JiXadS7afSnKxYtgxeXJ3ihCxhJrzQDeVCcx0YAL60SrntqJnj\ncyj3mS85fvsLYBSVo3QI+BWU+82rVt2vAC2rlP06cNGq/zuohZSfQaWEsK9pu/u05dT7sTzlltVZ\naV5W46QYXlxOHmxOKtV+qsBJyyplS+ZlPTmpdZ3ihse7cOHCxQZELU1YunDhwoWLAuEqbxcuXLjY\ngHCVtwsXLlxsQLjK24ULFy42IFzl7cKFCxcbEK7yduHChYsNCFd5u3DhwsUGhKu8Xbhw4WID4n8D\nwGEj/FdUiQMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114aa0050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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S5tGPvgt/dLe4EiFVBdwmksek4rCXxi/UsfPlSxx74Vu4RqYZL/DiLjyFE6YEeY+K5ZGp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\nwbpuIeITSD130/T4e+DEaUUuzlcjC8Y11tjBGu2w0gwhD1OZFi6ag3S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      "text/plain": [
       "<matplotlib.figure.Figure at 0x114d72810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Lfg4DHwVeq8FsASr63/Z8VnuSt5Ty+frm6F58AfhY/fP/iwq6vOXkrR59y7Ro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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1150e4f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sDs/0Ps+pPjg7OG/p1ZAhzs5aFfthjanxVk597wTdMw4k4yRYWEC/Enyk5P/P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\nASZP9qA4A0rJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11563cf10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PKC9Q7NgF0bSlEg4dJBVTGH2/BG3cjGQQwlto2svBvQvKfg48bwaRrlyGmSqKXpzM4stX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\nXt5pTu7S9l0xVu6n2uQIMKIoyqSiKBnge8DTd2mvD2NbhqIoZ1FTAOv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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1159bf790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib_pyplot_samples(train_datasets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cYznx5jUwzoO"
   },
   "source": [
    "---\n",
    "Problem 3\n",
    "---------\n",
    "Another check: we expect the data to be balanced across classes. Verify that.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/A.pickle 52909\n",
      "notMNIST_large/B.pickle 52911\n",
      "notMNIST_large/C.pickle 52912\n",
      "notMNIST_large/D.pickle 52911\n",
      "notMNIST_large/E.pickle 52912\n",
      "notMNIST_large/F.pickle 52912\n",
      "notMNIST_large/G.pickle 52912\n",
      "notMNIST_large/H.pickle 52912\n",
      "notMNIST_large/I.pickle 52912\n",
      "notMNIST_large/J.pickle 52911\n",
      "notMNIST_small/A.pickle 1872\n",
      "notMNIST_small/B.pickle 1873\n",
      "notMNIST_small/C.pickle 1873\n",
      "notMNIST_small/D.pickle 1873\n",
      "notMNIST_small/E.pickle 1873\n",
      "notMNIST_small/F.pickle 1872\n",
      "notMNIST_small/G.pickle 1872\n",
      "notMNIST_small/H.pickle 1872\n",
      "notMNIST_small/I.pickle 1872\n",
      "notMNIST_small/J.pickle 1872\n"
     ]
    }
   ],
   "source": [
    "def balance_check(pkls):    \n",
    "    for pkl in pkls:\n",
    "        with open(pkl, 'r') as f:\n",
    "            dataset = pickle.load(f)\n",
    "        print (pkl, len(dataset))\n",
    "\n",
    "balance_check(train_datasets)\n",
    "balance_check(test_datasets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LA7M7K22ynCt"
   },
   "source": [
    "Merge and prune the training data as needed. Depending on your computer setup, you might not be able to fit it all in memory, and you can tune `train_size` as needed. The labels will be stored into a separate array of integers 0 through 9.\n",
    "\n",
    "Also create a validation dataset for hyperparameter tuning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 411281,
     "status": "ok",
     "timestamp": 1444485897869,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2a0a5e044bb03b66",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "s3mWgZLpyuzq",
    "outputId": "8af66da6-902d-4719-bedc-7c9fb7ae7948"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training: (200000, 28, 28) (200000,)\n",
      "Validation: (10000, 28, 28) (10000,)\n",
      "Testing: (10000, 28, 28) (10000,)\n"
     ]
    }
   ],
   "source": [
    "def make_arrays(nb_rows, img_size):\n",
    "    if nb_rows:\n",
    "        dataset = np.ndarray((nb_rows, img_size, img_size), dtype=np.float32)\n",
    "        labels = np.ndarray(nb_rows, dtype=np.int32)\n",
    "    else:\n",
    "        dataset, labels = None, None\n",
    "    return dataset, labels\n",
    "\n",
    "def merge_datasets(pickle_files, train_size, valid_size=0):\n",
    "    num_classes = len(pickle_files)\n",
    "    valid_dataset, valid_labels = make_arrays(valid_size, image_size)\n",
    "    train_dataset, train_labels = make_arrays(train_size, image_size)\n",
    "    vsize_per_class = valid_size // num_classes\n",
    "    tsize_per_class = train_size // num_classes\n",
    "    \n",
    "    start_v, start_t = 0, 0\n",
    "    end_v, end_t = vsize_per_class, tsize_per_class\n",
    "    end_l = vsize_per_class+tsize_per_class\n",
    "    for label, pickle_file in enumerate(pickle_files):       \n",
    "        try:\n",
    "            with open(pickle_file, 'rb') as f:\n",
    "                letter_set = pickle.load(f)\n",
    "                # let's shuffle the letters to have random validation and training set\n",
    "                np.random.shuffle(letter_set)\n",
    "            if valid_dataset is not None:\n",
    "                valid_letter = letter_set[:vsize_per_class, :, :]\n",
    "                valid_dataset[start_v:end_v, :, :] = valid_letter\n",
    "                valid_labels[start_v:end_v] = label\n",
    "                start_v += vsize_per_class\n",
    "                end_v += vsize_per_class\n",
    "                    \n",
    "            train_letter = letter_set[vsize_per_class:end_l, :, :]\n",
    "            train_dataset[start_t:end_t, :, :] = train_letter\n",
    "            train_labels[start_t:end_t] = label\n",
    "            start_t += tsize_per_class\n",
    "            end_t += tsize_per_class\n",
    "        except Exception as e:\n",
    "            print('Unable to process data from', pickle_file, ':', e)\n",
    "            raise\n",
    "    \n",
    "    return valid_dataset, valid_labels, train_dataset, train_labels\n",
    "            \n",
    "            \n",
    "train_size = 200000\n",
    "valid_size = 10000\n",
    "test_size = 10000\n",
    "\n",
    "valid_dataset, valid_labels, train_dataset, train_labels = merge_datasets(\n",
    "  train_datasets, train_size, valid_size)\n",
    "_, _, test_dataset, test_labels = merge_datasets(test_datasets, test_size)\n",
    "\n",
    "print('Training:', train_dataset.shape, train_labels.shape)\n",
    "print('Validation:', valid_dataset.shape, valid_labels.shape)\n",
    "print('Testing:', test_dataset.shape, test_labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GPTCnjIcyuKN"
   },
   "source": [
    "Next, we'll randomize the data. It's important to have the labels well shuffled for the training and test distributions to match."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "6WZ2l2tN2zOL"
   },
   "outputs": [],
   "source": [
    "def randomize(dataset, labels):\n",
    "    permutation = np.random.permutation(labels.shape[0])\n",
    "    shuffled_dataset = dataset[permutation,:,:]\n",
    "    shuffled_labels = labels[permutation]\n",
    "    return shuffled_dataset, shuffled_labels\n",
    "train_dataset, train_labels = randomize(train_dataset, train_labels)\n",
    "test_dataset, test_labels = randomize(test_dataset, test_labels)\n",
    "valid_dataset, valid_labels = randomize(valid_dataset, valid_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "puDUTe6t6USl"
   },
   "source": [
    "---\n",
    "Problem 4\n",
    "---------\n",
    "Convince yourself that the data is still good after shuffling!\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To be sure that the data are still fine after the merger and the randomization, I will select one item and display the image alongside the label. Note: 0 = A, 1 = B, 2 = C, 3 = D, 4 = E, 5 = F, 6 = G, 7 = H, 8 = I, 9 = J. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pretty_labels = {0: 'A', 1: 'B', 2: 'C', 3: 'D', 4: 'E', 5: 'F', 6: 'G', 7: 'H', 8: 'I', 9: 'J'}\n",
    "\n",
    "def disp_sample_dataset(dataset, labels):\n",
    "    items = random.sample(range(len(labels)), 8)\n",
    "    for i, item in enumerate(items):\n",
    "        plt.subplot(2, 4, i+1)\n",
    "        plt.axis('off')\n",
    "        plt.title(pretty_labels[labels[item]])\n",
    "        plt.imshow(dataset[item])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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GP/z8j1iTaRa8PkpbcWrTl1irj9KXWiHev8Cl7DzVElRKYNUVhnTp9VzUWgH/8yX8pKK3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\nBqQ9wAADDPASYUDaAwwwwAAvEf5/MxB/y1s+LtYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11786fad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp_sample_dataset(train_dataset, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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h4zVJO+L5MFQL2fG6+3Q+Y980ceI29VSSWixOPbDxPJdQLBO0u/JuyKBFW7RD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\niFcIIWmHCBEixCuEkLRDhAgR4hXC/wcyQ66NRGjn3AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d266950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp_sample_dataset(valid_dataset, valid_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MdC9vd4Z2+NgGIkPXtaC2DHadBbPI368OcC/zavDAw2Xd5ijB/fVERvk3womr9gCLVh5a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\niUQiOUFI0ZZIJJITxP8DlijuGUR8qXEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116d432d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp_sample_dataset(test_dataset, test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tIQJaJuwg5Hw"
   },
   "source": [
    "Finally, let's save the data for later reuse:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "QiR_rETzem6C"
   },
   "outputs": [],
   "source": [
    "pickle_file = 'notMNIST.pickle'\n",
    "\n",
    "try:\n",
    "    f = open(pickle_file, 'wb')\n",
    "    save = {\n",
    "    'train_dataset': train_dataset,\n",
    "    'train_labels': train_labels,\n",
    "    'valid_dataset': valid_dataset,\n",
    "    'valid_labels': valid_labels,\n",
    "    'test_dataset': test_dataset,\n",
    "    'test_labels': test_labels,\n",
    "    }\n",
    "    pickle.dump(save, f, pickle.HIGHEST_PROTOCOL)\n",
    "    f.close()\n",
    "except Exception as e:\n",
    "    print('Unable to save data to', pickle_file, ':', e)\n",
    "    raise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 413065,
     "status": "ok",
     "timestamp": 1444485899688,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2a0a5e044bb03b66",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "hQbLjrW_iT39",
    "outputId": "b440efc6-5ee1-4cbc-d02d-93db44ebd956"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compressed pickle size: 690800441\n"
     ]
    }
   ],
   "source": [
    "statinfo = os.stat(pickle_file)\n",
    "print('Compressed pickle size:', statinfo.st_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gE_cRAQB33lk"
   },
   "source": [
    "---\n",
    "Problem 5\n",
    "---------\n",
    "\n",
    "By construction, this dataset might contain a lot of overlapping samples, including training data that's also contained in the validation and test set! Overlap between training and test can skew the results if you expect to use your model in an environment where there is never an overlap, but are actually ok if you expect to see training samples recur when you use it.\n",
    "Measure how much overlap there is between training, validation and test samples.\n",
    "\n",
    "Optional questions:\n",
    "- What about near duplicates between datasets? (images that are almost identical)\n",
    "- Create a sanitized validation and test set, and compare your accuracy on those in subsequent assignments.\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# I dont think doing number 5 is currently useful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\npickle_file = 'notMNIST2.pickle'\\n\\ntry:\\n  f = open(pickle_file, 'wb')\\n  save = {\\n    'train_dataset': train_dataset,\\n    'train_labels': train_labels,\\n    'valid_dataset': valid_dataset,\\n    'valid_labels': valid_labels,\\n    'test_dataset': test_dataset,\\n    'test_labels': test_labels,\\n    'new_valid_dataset': new_valid_dataset,\\n    'new_valid_labels': new_valid_labels,\\n    'new_test_dataset': new_test_dataset,\\n    'new_test_labels': new_test_labels,\\n    }\\n  pickle.dump(save, f, pickle.HIGHEST_PROTOCOL)\\n  f.close()\\nexcept Exception as e:\\n  print('Unable to save data to', pickle_file, ':', e)\\n  raise  \\n\""
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "pickle_file = 'notMNIST2.pickle'\n",
    "\n",
    "try:\n",
    "  f = open(pickle_file, 'wb')\n",
    "  save = {\n",
    "    'train_dataset': train_dataset,\n",
    "    'train_labels': train_labels,\n",
    "    'valid_dataset': valid_dataset,\n",
    "    'valid_labels': valid_labels,\n",
    "    'test_dataset': test_dataset,\n",
    "    'test_labels': test_labels,\n",
    "    'new_valid_dataset': new_valid_dataset,\n",
    "    'new_valid_labels': new_valid_labels,\n",
    "    'new_test_dataset': new_test_dataset,\n",
    "    'new_test_labels': new_test_labels,\n",
    "    }\n",
    "  pickle.dump(save, f, pickle.HIGHEST_PROTOCOL)\n",
    "  f.close()\n",
    "except Exception as e:\n",
    "  print('Unable to save data to', pickle_file, ':', e)\n",
    "  raise  \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "L8oww1s4JMQx"
   },
   "source": [
    "---\n",
    "Problem 6\n",
    "---------\n",
    "\n",
    "Let's get an idea of what an off-the-shelf classifier can give you on this data. It's always good to check that there is something to learn, and that it's a problem that is not so trivial that a canned solution solves it.\n",
    "\n",
    "Train a simple model on this data using 50, 100, 1000 and 5000 training samples. Hint: you can use the LogisticRegression model from sklearn.linear_model.\n",
    "\n",
    "Optional question: train an off-the-shelf model on all the data!\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "regr = LogisticRegression()\n",
    "X_test = test_dataset.reshape(test_dataset.shape[0], 28 * 28)\n",
    "y_test = test_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'>Note on the Following Training Sample Code</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code uses score which returns the mean accuracy on the given test data and labels. In multi-label classification (which is what question 6 is doing), the subset accuracy is a harsh metric since each label needs to be correctly predicted. \n",
    "\n",
    "Later on in the jupyter notebook, there will be code showing on which class the model is consistently predicting incorrectly. This is called the confusion matrix. \n",
    "\n",
    "http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'>50 Training Samples</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 47.6 ms, sys: 1.29 ms, total: 48.9 ms\n",
      "Wall time: 47.8 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.627"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_size = 50\n",
    "X_train = train_dataset[:sample_size].reshape(sample_size, 784)\n",
    "y_train = train_labels[:sample_size]\n",
    "%time regr.fit(X_train, y_train)\n",
    "regr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'>100 Training Samples</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 118 ms, sys: 1.38 ms, total: 119 ms\n",
      "Wall time: 118 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.74299999999999999"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_size = 100\n",
    "X_train = train_dataset[:sample_size].reshape(sample_size, 784)\n",
    "y_train = train_labels[:sample_size]\n",
    "%time regr.fit(X_train, y_train)\n",
    "regr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<h3 align='Left'>1000 Training Samples</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.05 s, sys: 9.11 ms, total: 2.06 s\n",
      "Wall time: 2.06 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.82679999999999998"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_size = 1000\n",
    "X_train = train_dataset[:sample_size].reshape(sample_size, 784)\n",
    "y_train = train_labels[:sample_size]\n",
    "%time regr.fit(X_train, y_train)\n",
    "regr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'>5000 Training Samples plus Predicting Class Labels </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 17.2 s, sys: 31 ms, total: 17.2 s\n",
      "Wall time: 17.2 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.85540000000000005"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_size = 5000\n",
    "X_train = train_dataset[:sample_size].reshape(sample_size, 784)\n",
    "y_train = train_labels[:sample_size]\n",
    "%time regr.fit(X_train, y_train)\n",
    "regr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict class labels for the test set\n",
    "predicted = regr.predict(X_test)\n",
    "predicted[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.64654384e-01,   4.21571315e-01,   9.81267908e-05,\n",
       "         5.62562119e-03,   1.31325110e-03,   5.85427878e-03,\n",
       "         3.58721021e-03,   4.83213803e-04,   2.36878287e-01,\n",
       "         1.59934312e-01])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr.predict_proba(X_test)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'>Confusion Matrix </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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hgVPSHsCngLER8TayGu6UIu9pZtXWzGXlirI1HtUHAdtL2gxsBzyzFe5pZhVV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\nmpnl5MBpZpbT/wAAVcAlSyv7MwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116540a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized confusion matrix\n",
      "[[ 0.88  0.01  0.01  0.01  0.01  0.01  0.01  0.03  0.01  0.02]\n",
      " [ 0.01  0.85  0.01  0.03  0.02  0.01  0.02  0.01  0.02  0.01]\n",
      " [ 0.    0.01  0.89  0.01  0.02  0.01  0.02  0.    0.01  0.01]\n",
      " [ 0.    0.03  0.01  0.88  0.01  0.01  0.01  0.01  0.02  0.02]\n",
      " [ 0.01  0.02  0.05  0.01  0.77  0.04  0.02  0.01  0.05  0.01]\n",
      " [ 0.01  0.01  0.01  0.01  0.01  0.9   0.01  0.01  0.02  0.02]\n",
      " [ 0.01  0.01  0.04  0.01  0.01  0.02  0.85  0.01  0.01  0.01]\n",
      " [ 0.03  0.02  0.01  0.01  0.03  0.03  0.01  0.83  0.02  0.02]\n",
      " [ 0.02  0.01  0.01  0.01  0.02  0.02  0.02  0.02  0.83  0.05]\n",
      " [ 0.01  0.01  0.02  0.02  0.    0.02  0.01  0.02  0.04  0.87]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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TpZmVi0/DzcxycMvSzCwH91mameVQ0lw5MJLl+oJHSKy+u9gRNiMOP7vQ+lff/vlC6y/6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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1150c9a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "# Using Matplotlib. The confusion matrix that will result from this will be a heatmap. \n",
    "def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues):\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('Actual label')\n",
    "    plt.xlabel('Predicted label')\n",
    "\n",
    "cm = metrics.confusion_matrix(y_test, predicted)\n",
    "np.set_printoptions(precision=2)\n",
    "print ('Confusion Matrix, without normalization')\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm)\n",
    "plt.show()\n",
    "\n",
    "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "print('Normalized confusion matrix')\n",
    "print(cm_normalized)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm_normalized, title='Normalized confusion matrix')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'>All Training Samples</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8min 36s, sys: 780 ms, total: 8min 37s\n",
      "Wall time: 8min 37s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.89590000000000003"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr2 = LogisticRegression(solver='sag')\n",
    "sample_size = len(train_dataset)\n",
    "X_train = train_dataset[:sample_size].reshape(sample_size, 784)\n",
    "y_train = train_labels[:sample_size]\n",
    "%time regr2.fit(X_train, y_train)\n",
    "regr2.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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t0CnpH7zcERD4mWu4Wp3t5hYPvrzLcBOK652AlXBtlXC7g+iROGDXFbgeo5kc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1AAAg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\nqTUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116a2d990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_labels = regr2.predict(X_test)\n",
    "disp_sample_dataset(test_dataset, pred_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# http://scikit-learn.org/stable/auto_examples/linear_model/plot_iris_logistic.html"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "default_view": {},
   "name": "1_notmnist.ipynb",
   "provenance": [],
   "version": "0.3.2",
   "views": {}
  },
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
